diff --git a/lapack-netlib/SRC/cgejsv.f b/lapack-netlib/SRC/cgejsv.f index 062ac182b..8fe4159f6 100644 --- a/lapack-netlib/SRC/cgejsv.f +++ b/lapack-netlib/SRC/cgejsv.f @@ -52,10 +52,10 @@ *> are computed and stored in the arrays U and V, respectively. The diagonal *> of [SIGMA] is computed and stored in the array SVA. *> \endverbatim -*> -*> Arguments: -*> ========== -*> +* +* Arguments: +* ========== +* *> \param[in] JOBA *> \verbatim *> JOBA is CHARACTER*1 @@ -151,7 +151,7 @@ *> transposed A if A^* seems to be better with respect to convergence. *> If the matrix is not square, JOBT is ignored. *> The decision is based on two values of entropy over the adjoint -*> orbit of A^* * A. See the descriptions of WORK(6) and WORK(7). +*> orbit of A^* * A. See the descriptions of RWORK(6) and RWORK(7). *> = 'T': transpose if entropy test indicates possibly faster *> convergence of Jacobi process if A^* is taken as input. If A is *> replaced with A^*, then the row pivoting is included automatically. @@ -209,11 +209,11 @@ *> \verbatim *> SVA is REAL array, dimension (N) *> On exit, -*> - For WORK(1)/WORK(2) = ONE: The singular values of A. During the -*> computation SVA contains Euclidean column norms of the +*> - For RWORK(1)/RWORK(2) = ONE: The singular values of A. During +*> the computation SVA contains Euclidean column norms of the *> iterated matrices in the array A. -*> - For WORK(1) .NE. WORK(2): The singular values of A are -*> (WORK(1)/WORK(2)) * SVA(1:N). This factored form is used if +*> - For RWORK(1) .NE. RWORK(2): The singular values of A are +*> (RWORK(1)/RWORK(2)) * SVA(1:N). This factored form is used if *> sigma_max(A) overflows or if small singular values have been *> saved from underflow by scaling the input matrix A. *> - If JOBR='R' then some of the singular values may be returned diff --git a/lapack-netlib/SRC/claswlq.f b/lapack-netlib/SRC/claswlq.f index 63cbd02c6..1a09b8305 100644 --- a/lapack-netlib/SRC/claswlq.f +++ b/lapack-netlib/SRC/claswlq.f @@ -104,6 +104,7 @@ *> \endverbatim *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= MB*M. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/clatsqr.f b/lapack-netlib/SRC/clatsqr.f index 7a0b85487..377190081 100644 --- a/lapack-netlib/SRC/clatsqr.f +++ b/lapack-netlib/SRC/clatsqr.f @@ -106,6 +106,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= NB*N. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/ctgevc.f b/lapack-netlib/SRC/ctgevc.f index 4e5289cb2..22144f259 100644 --- a/lapack-netlib/SRC/ctgevc.f +++ b/lapack-netlib/SRC/ctgevc.f @@ -53,7 +53,7 @@ *> *> S*x = w*P*x, (y**H)*S = w*(y**H)*P, *> -*> where y**H denotes the conjugate tranpose of y. +*> where y**H denotes the conjugate transpose of y. *> The eigenvalues are not input to this routine, but are computed *> directly from the diagonal elements of S and P. *> @@ -154,7 +154,7 @@ *> \verbatim *> VR is COMPLEX array, dimension (LDVR,MM) *> On entry, if SIDE = 'R' or 'B' and HOWMNY = 'B', VR must -*> contain an N-by-N matrix Q (usually the unitary matrix Z +*> contain an N-by-N matrix Z (usually the unitary matrix Z *> of right Schur vectors returned by CHGEQZ). *> On exit, if SIDE = 'R' or 'B', VR contains: *> if HOWMNY = 'A', the matrix X of right eigenvectors of (S,P); @@ -259,7 +259,7 @@ EXTERNAL LSAME, SLAMCH, CLADIV * .. * .. External Subroutines .. - EXTERNAL CGEMV, SLABAD, XERBLA + EXTERNAL CGEMV, XERBLA * .. * .. Intrinsic Functions .. INTRINSIC ABS, AIMAG, CMPLX, CONJG, MAX, MIN, REAL @@ -367,7 +367,6 @@ * SAFMIN = SLAMCH( 'Safe minimum' ) BIG = ONE / SAFMIN - CALL SLABAD( SAFMIN, BIG ) ULP = SLAMCH( 'Epsilon' )*SLAMCH( 'Base' ) SMALL = SAFMIN*N / ULP BIG = ONE / SMALL diff --git a/lapack-netlib/SRC/cuncsd2by1.f b/lapack-netlib/SRC/cuncsd2by1.f index 8360d5932..f0c44f670 100644 --- a/lapack-netlib/SRC/cuncsd2by1.f +++ b/lapack-netlib/SRC/cuncsd2by1.f @@ -212,13 +212,13 @@ *> LRWORK is INTEGER *> The dimension of the array RWORK. *> -*> If LRWORK = -1, then a workspace query is assumed; the routine +*> If LRWORK=-1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK and RWORK *> arrays, returns this value as the first entry of the WORK *> and RWORK array, respectively, and no error message related *> to LWORK or LRWORK is issued by XERBLA. *> \endverbatim -* +*> *> \param[out] IWORK *> \verbatim *> IWORK is INTEGER array, dimension (M-MIN(P,M-P,Q,M-Q)) diff --git a/lapack-netlib/SRC/cungtsqr.f b/lapack-netlib/SRC/cungtsqr.f index 64ccb9731..1734be2aa 100644 --- a/lapack-netlib/SRC/cungtsqr.f +++ b/lapack-netlib/SRC/cungtsqr.f @@ -133,6 +133,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= (M+NB)*N. *> If LWORK = -1, then a workspace query is assumed. *> The routine only calculates the optimal size of the WORK @@ -302,4 +303,4 @@ * * End of CUNGTSQR * - END \ No newline at end of file + END diff --git a/lapack-netlib/SRC/dlaswlq.f b/lapack-netlib/SRC/dlaswlq.f index fb8857145..c95c94cbc 100644 --- a/lapack-netlib/SRC/dlaswlq.f +++ b/lapack-netlib/SRC/dlaswlq.f @@ -104,6 +104,7 @@ *> \endverbatim *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= MB*M. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/dlatsqr.f b/lapack-netlib/SRC/dlatsqr.f index f5cbb76bb..94a04be02 100644 --- a/lapack-netlib/SRC/dlatsqr.f +++ b/lapack-netlib/SRC/dlatsqr.f @@ -106,6 +106,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= NB*N. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/dorgtsqr.f b/lapack-netlib/SRC/dorgtsqr.f index 34d96b238..2e5cd40be 100644 --- a/lapack-netlib/SRC/dorgtsqr.f +++ b/lapack-netlib/SRC/dorgtsqr.f @@ -133,6 +133,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= (M+NB)*N. *> If LWORK = -1, then a workspace query is assumed. *> The routine only calculates the optimal size of the WORK @@ -301,4 +302,4 @@ * * End of DORGTSQR * - END \ No newline at end of file + END diff --git a/lapack-netlib/SRC/slaswlq.f b/lapack-netlib/SRC/slaswlq.f index bc7cf343d..95e0ddcce 100644 --- a/lapack-netlib/SRC/slaswlq.f +++ b/lapack-netlib/SRC/slaswlq.f @@ -104,6 +104,7 @@ *> \endverbatim *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= MB * M. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/slatsqr.f b/lapack-netlib/SRC/slatsqr.f index 33966c01f..86733bb15 100644 --- a/lapack-netlib/SRC/slatsqr.f +++ b/lapack-netlib/SRC/slatsqr.f @@ -106,6 +106,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= NB*N. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/sorgtsqr.f b/lapack-netlib/SRC/sorgtsqr.f index a755fa4a4..692eba1d9 100644 --- a/lapack-netlib/SRC/sorgtsqr.f +++ b/lapack-netlib/SRC/sorgtsqr.f @@ -133,6 +133,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= (M+NB)*N. *> If LWORK = -1, then a workspace query is assumed. *> The routine only calculates the optimal size of the WORK @@ -301,4 +302,4 @@ * * End of SORGTSQR * - END \ No newline at end of file + END diff --git a/lapack-netlib/SRC/zgejsv.f b/lapack-netlib/SRC/zgejsv.f index 5134ea891..464c4a0fa 100644 --- a/lapack-netlib/SRC/zgejsv.f +++ b/lapack-netlib/SRC/zgejsv.f @@ -52,10 +52,10 @@ *> are computed and stored in the arrays U and V, respectively. The diagonal *> of [SIGMA] is computed and stored in the array SVA. *> \endverbatim -*> -*> Arguments: -*> ========== -*> +* +* Arguments: +* ========== +* *> \param[in] JOBA *> \verbatim *> JOBA is CHARACTER*1 @@ -151,7 +151,7 @@ *> transposed A if A^* seems to be better with respect to convergence. *> If the matrix is not square, JOBT is ignored. *> The decision is based on two values of entropy over the adjoint -*> orbit of A^* * A. See the descriptions of WORK(6) and WORK(7). +*> orbit of A^* * A. See the descriptions of RWORK(6) and RWORK(7). *> = 'T': transpose if entropy test indicates possibly faster *> convergence of Jacobi process if A^* is taken as input. If A is *> replaced with A^*, then the row pivoting is included automatically. @@ -209,11 +209,11 @@ *> \verbatim *> SVA is DOUBLE PRECISION array, dimension (N) *> On exit, -*> - For WORK(1)/WORK(2) = ONE: The singular values of A. During the -*> computation SVA contains Euclidean column norms of the +*> - For RWORK(1)/RWORK(2) = ONE: The singular values of A. During +*> the computation SVA contains Euclidean column norms of the *> iterated matrices in the array A. -*> - For WORK(1) .NE. WORK(2): The singular values of A are -*> (WORK(1)/WORK(2)) * SVA(1:N). This factored form is used if +*> - For RWORK(1) .NE. RWORK(2): The singular values of A are +*> (RWORK(1)/RWORK(2)) * SVA(1:N). This factored form is used if *> sigma_max(A) overflows or if small singular values have been *> saved from underflow by scaling the input matrix A. *> - If JOBR='R' then some of the singular values may be returned diff --git a/lapack-netlib/SRC/zlaswlq.f b/lapack-netlib/SRC/zlaswlq.f index e4e703343..be4c48539 100644 --- a/lapack-netlib/SRC/zlaswlq.f +++ b/lapack-netlib/SRC/zlaswlq.f @@ -104,6 +104,7 @@ *> \endverbatim *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= MB*M. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/zlatsqr.f b/lapack-netlib/SRC/zlatsqr.f index ffdbc68c0..8c938aebc 100644 --- a/lapack-netlib/SRC/zlatsqr.f +++ b/lapack-netlib/SRC/zlatsqr.f @@ -106,6 +106,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= NB*N. *> If LWORK = -1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK array, returns diff --git a/lapack-netlib/SRC/ztgevc.f b/lapack-netlib/SRC/ztgevc.f index 23bd36ddb..6dcbe582f 100644 --- a/lapack-netlib/SRC/ztgevc.f +++ b/lapack-netlib/SRC/ztgevc.f @@ -53,7 +53,7 @@ *> *> S*x = w*P*x, (y**H)*S = w*(y**H)*P, *> -*> where y**H denotes the conjugate tranpose of y. +*> where y**H denotes the conjugate transpose of y. *> The eigenvalues are not input to this routine, but are computed *> directly from the diagonal elements of S and P. *> @@ -154,7 +154,7 @@ *> \verbatim *> VR is COMPLEX*16 array, dimension (LDVR,MM) *> On entry, if SIDE = 'R' or 'B' and HOWMNY = 'B', VR must -*> contain an N-by-N matrix Q (usually the unitary matrix Z +*> contain an N-by-N matrix Z (usually the unitary matrix Z *> of right Schur vectors returned by ZHGEQZ). *> On exit, if SIDE = 'R' or 'B', VR contains: *> if HOWMNY = 'A', the matrix X of right eigenvectors of (S,P); @@ -259,7 +259,7 @@ EXTERNAL LSAME, DLAMCH, ZLADIV * .. * .. External Subroutines .. - EXTERNAL DLABAD, XERBLA, ZGEMV + EXTERNAL XERBLA, ZGEMV * .. * .. Intrinsic Functions .. INTRINSIC ABS, DBLE, DCMPLX, DCONJG, DIMAG, MAX, MIN @@ -367,7 +367,6 @@ * SAFMIN = DLAMCH( 'Safe minimum' ) BIG = ONE / SAFMIN - CALL DLABAD( SAFMIN, BIG ) ULP = DLAMCH( 'Epsilon' )*DLAMCH( 'Base' ) SMALL = SAFMIN*N / ULP BIG = ONE / SMALL diff --git a/lapack-netlib/SRC/zuncsd2by1.f b/lapack-netlib/SRC/zuncsd2by1.f index 2d861c2cc..399b598be 100644 --- a/lapack-netlib/SRC/zuncsd2by1.f +++ b/lapack-netlib/SRC/zuncsd2by1.f @@ -211,13 +211,13 @@ *> LRWORK is INTEGER *> The dimension of the array RWORK. *> -*> If LRWORK = -1, then a workspace query is assumed; the routine +*> If LRWORK=-1, then a workspace query is assumed; the routine *> only calculates the optimal size of the WORK and RWORK *> arrays, returns this value as the first entry of the WORK *> and RWORK array, respectively, and no error message related *> to LWORK or LRWORK is issued by XERBLA. *> \endverbatim -* +*> *> \param[out] IWORK *> \verbatim *> IWORK is INTEGER array, dimension (M-MIN(P,M-P,Q,M-Q)) diff --git a/lapack-netlib/SRC/zungtsqr.f b/lapack-netlib/SRC/zungtsqr.f index 4f2d7dfdd..23e28ac5c 100644 --- a/lapack-netlib/SRC/zungtsqr.f +++ b/lapack-netlib/SRC/zungtsqr.f @@ -133,6 +133,7 @@ *> *> \param[in] LWORK *> \verbatim +*> LWORK is INTEGER *> The dimension of the array WORK. LWORK >= (M+NB)*N. *> If LWORK = -1, then a workspace query is assumed. *> The routine only calculates the optimal size of the WORK @@ -302,4 +303,4 @@ * * End of ZUNGTSQR * - END \ No newline at end of file + END diff --git a/lapack-netlib/cgbsvx.f b/lapack-netlib/cgbsvx.f new file mode 100644 index 000000000..eaab5682c --- /dev/null +++ b/lapack-netlib/cgbsvx.f @@ -0,0 +1,644 @@ +*> \brief CGBSVX computes the solution to system of linear equations A * X = B for GB matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download CGBSVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE CGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, +* LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, +* RCOND, FERR, BERR, WORK, RWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS +* REAL RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ) +* REAL BERR( * ), C( * ), FERR( * ), R( * ), +* $ RWORK( * ) +* COMPLEX AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> CGBSVX uses the LU factorization to compute the solution to a complex +*> system of linear equations A * X = B, A**T * X = B, or A**H * X = B, +*> where A is a band matrix of order N with KL subdiagonals and KU +*> superdiagonals, and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed by this subroutine: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = L * U, +*> where L is a product of permutation and unit lower triangular +*> matrices with KL subdiagonals, and U is upper triangular with +*> KL+KU superdiagonals. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AFB and IPIV contain the factored form of +*> A. If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> AB, AFB, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AFB and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AFB and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations. +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Conjugate transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] KL +*> \verbatim +*> KL is INTEGER +*> The number of subdiagonals within the band of A. KL >= 0. +*> \endverbatim +*> +*> \param[in] KU +*> \verbatim +*> KU is INTEGER +*> The number of superdiagonals within the band of A. KU >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] AB +*> \verbatim +*> AB is COMPLEX array, dimension (LDAB,N) +*> On entry, the matrix A in band storage, in rows 1 to KL+KU+1. +*> The j-th column of A is stored in the j-th column of the +*> array AB as follows: +*> AB(KU+1+i-j,j) = A(i,j) for max(1,j-KU)<=i<=min(N,j+kl) +*> +*> If FACT = 'F' and EQUED is not 'N', then A must have been +*> equilibrated by the scaling factors in R and/or C. AB is not +*> modified if FACT = 'F' or 'N', or if FACT = 'E' and +*> EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDAB +*> \verbatim +*> LDAB is INTEGER +*> The leading dimension of the array AB. LDAB >= KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] AFB +*> \verbatim +*> AFB is COMPLEX array, dimension (LDAFB,N) +*> If FACT = 'F', then AFB is an input argument and on entry +*> contains details of the LU factorization of the band matrix +*> A, as computed by CGBTRF. U is stored as an upper triangular +*> band matrix with KL+KU superdiagonals in rows 1 to KL+KU+1, +*> and the multipliers used during the factorization are stored +*> in rows KL+KU+2 to 2*KL+KU+1. If EQUED .ne. 'N', then AFB is +*> the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AFB is an output argument and on exit +*> returns details of the LU factorization of A. +*> +*> If FACT = 'E', then AFB is an output argument and on exit +*> returns details of the LU factorization of the equilibrated +*> matrix A (see the description of AB for the form of the +*> equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAFB +*> \verbatim +*> LDAFB is INTEGER +*> The leading dimension of the array AFB. LDAFB >= 2*KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = L*U +*> as computed by CGBTRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is REAL array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is REAL array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is COMPLEX array, dimension (LDB,NRHS) +*> On entry, the right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is COMPLEX array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is REAL +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is REAL array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is REAL array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is COMPLEX array, dimension (2*N) +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is REAL array, dimension (MAX(1,N)) +*> On exit, RWORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If RWORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 RWORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization +*> has been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complexGBsolve +* +* ===================================================================== + SUBROUTINE CGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, + $ LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, + $ RCOND, FERR, BERR, WORK, RWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS + REAL RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ) + REAL BERR( * ), C( * ), FERR( * ), R( * ), + $ RWORK( * ) + COMPLEX AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* Moved setting of INFO = N+1 so INFO does not subsequently get +* overwritten. Sven, 17 Mar 05. +* ===================================================================== +* +* .. Parameters .. + REAL ZERO, ONE + PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J, J1, J2 + REAL AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + REAL CLANGB, CLANTB, SLAMCH + EXTERNAL LSAME, CLANGB, CLANTB, SLAMCH +* .. +* .. External Subroutines .. + EXTERNAL CCOPY, CGBCON, CGBEQU, CGBRFS, CGBTRF, CGBTRS, + $ CLACPY, CLAQGB, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC ABS, MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = SLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( KL.LT.0 ) THEN + INFO = -4 + ELSE IF( KU.LT.0 ) THEN + INFO = -5 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -6 + ELSE IF( LDAB.LT.KL+KU+1 ) THEN + INFO = -8 + ELSE IF( LDAFB.LT.2*KL+KU+1 ) THEN + INFO = -10 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -12 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -13 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -14 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -16 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -18 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'CGBSVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL CGBEQU( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL CLAQGB( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of the band matrix A. +* + DO 70 J = 1, N + J1 = MAX( J-KU, 1 ) + J2 = MIN( J+KL, N ) + CALL CCOPY( J2-J1+1, AB( KU+1-J+J1, J ), 1, + $ AFB( KL+KU+1-J+J1, J ), 1 ) + 70 CONTINUE +* + CALL CGBTRF( N, N, KL, KU, AFB, LDAFB, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + ANORM = ZERO + DO 90 J = 1, INFO + DO 80 I = MAX( KU+2-J, 1 ), MIN( N+KU+1-J, KL+KU+1 ) + ANORM = MAX( ANORM, ABS( AB( I, J ) ) ) + 80 CONTINUE + 90 CONTINUE + RPVGRW = CLANTB( 'M', 'U', 'N', INFO, MIN( INFO-1, KL+KU ), + $ AFB( MAX( 1, KL+KU+2-INFO ), 1 ), LDAFB, + $ RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ANORM / RPVGRW + END IF + RWORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = CLANGB( NORM, N, KL, KU, AB, LDAB, RWORK ) + RPVGRW = CLANTB( 'M', 'U', 'N', N, KL+KU, AFB, LDAFB, RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = CLANGB( 'M', N, KL, KU, AB, LDAB, RWORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL CGBCON( NORM, N, KL, KU, AFB, LDAFB, IPIV, ANORM, RCOND, + $ WORK, RWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL CLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL CGBTRS( TRANS, N, KL, KU, NRHS, AFB, LDAFB, IPIV, X, LDX, + $ INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL CGBRFS( TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, LDAFB, IPIV, + $ B, LDB, X, LDX, FERR, BERR, WORK, RWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 120 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 140 J = 1, NRHS + DO 130 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 130 CONTINUE + 140 CONTINUE + DO 150 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 150 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.SLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + RWORK( 1 ) = RPVGRW + RETURN +* +* End of CGBSVX +* + END diff --git a/lapack-netlib/cgejsv.f b/lapack-netlib/cgejsv.f new file mode 100644 index 000000000..51a6cee4e --- /dev/null +++ b/lapack-netlib/cgejsv.f @@ -0,0 +1,2232 @@ +*> \brief \b CGEJSV +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download CGEJSV + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE CGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, +* M, N, A, LDA, SVA, U, LDU, V, LDV, +* CWORK, LWORK, RWORK, LRWORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* IMPLICIT NONE +* INTEGER INFO, LDA, LDU, LDV, LWORK, M, N +* .. +* .. Array Arguments .. +* COMPLEX A( LDA, * ), U( LDU, * ), V( LDV, * ), CWORK( LWORK ) +* REAL SVA( N ), RWORK( LRWORK ) +* INTEGER IWORK( * ) +* CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> CGEJSV computes the singular value decomposition (SVD) of a complex M-by-N +*> matrix [A], where M >= N. The SVD of [A] is written as +*> +*> [A] = [U] * [SIGMA] * [V]^*, +*> +*> where [SIGMA] is an N-by-N (M-by-N) matrix which is zero except for its N +*> diagonal elements, [U] is an M-by-N (or M-by-M) unitary matrix, and +*> [V] is an N-by-N unitary matrix. The diagonal elements of [SIGMA] are +*> the singular values of [A]. The columns of [U] and [V] are the left and +*> the right singular vectors of [A], respectively. The matrices [U] and [V] +*> are computed and stored in the arrays U and V, respectively. The diagonal +*> of [SIGMA] is computed and stored in the array SVA. +*> \endverbatim +*> +*> Arguments: +*> ========== +*> +*> \param[in] JOBA +*> \verbatim +*> JOBA is CHARACTER*1 +*> Specifies the level of accuracy: +*> = 'C': This option works well (high relative accuracy) if A = B * D, +*> with well-conditioned B and arbitrary diagonal matrix D. +*> The accuracy cannot be spoiled by COLUMN scaling. The +*> accuracy of the computed output depends on the condition of +*> B, and the procedure aims at the best theoretical accuracy. +*> The relative error max_{i=1:N}|d sigma_i| / sigma_i is +*> bounded by f(M,N)*epsilon* cond(B), independent of D. +*> The input matrix is preprocessed with the QRF with column +*> pivoting. This initial preprocessing and preconditioning by +*> a rank revealing QR factorization is common for all values of +*> JOBA. Additional actions are specified as follows: +*> = 'E': Computation as with 'C' with an additional estimate of the +*> condition number of B. It provides a realistic error bound. +*> = 'F': If A = D1 * C * D2 with ill-conditioned diagonal scalings +*> D1, D2, and well-conditioned matrix C, this option gives +*> higher accuracy than the 'C' option. If the structure of the +*> input matrix is not known, and relative accuracy is +*> desirable, then this option is advisable. The input matrix A +*> is preprocessed with QR factorization with FULL (row and +*> column) pivoting. +*> = 'G': Computation as with 'F' with an additional estimate of the +*> condition number of B, where A=B*D. If A has heavily weighted +*> rows, then using this condition number gives too pessimistic +*> error bound. +*> = 'A': Small singular values are not well determined by the data +*> and are considered as noisy; the matrix is treated as +*> numerically rank deficient. The error in the computed +*> singular values is bounded by f(m,n)*epsilon*||A||. +*> The computed SVD A = U * S * V^* restores A up to +*> f(m,n)*epsilon*||A||. +*> This gives the procedure the licence to discard (set to zero) +*> all singular values below N*epsilon*||A||. +*> = 'R': Similar as in 'A'. Rank revealing property of the initial +*> QR factorization is used do reveal (using triangular factor) +*> a gap sigma_{r+1} < epsilon * sigma_r in which case the +*> numerical RANK is declared to be r. The SVD is computed with +*> absolute error bounds, but more accurately than with 'A'. +*> \endverbatim +*> +*> \param[in] JOBU +*> \verbatim +*> JOBU is CHARACTER*1 +*> Specifies whether to compute the columns of U: +*> = 'U': N columns of U are returned in the array U. +*> = 'F': full set of M left sing. vectors is returned in the array U. +*> = 'W': U may be used as workspace of length M*N. See the description +*> of U. +*> = 'N': U is not computed. +*> \endverbatim +*> +*> \param[in] JOBV +*> \verbatim +*> JOBV is CHARACTER*1 +*> Specifies whether to compute the matrix V: +*> = 'V': N columns of V are returned in the array V; Jacobi rotations +*> are not explicitly accumulated. +*> = 'J': N columns of V are returned in the array V, but they are +*> computed as the product of Jacobi rotations, if JOBT = 'N'. +*> = 'W': V may be used as workspace of length N*N. See the description +*> of V. +*> = 'N': V is not computed. +*> \endverbatim +*> +*> \param[in] JOBR +*> \verbatim +*> JOBR is CHARACTER*1 +*> Specifies the RANGE for the singular values. Issues the licence to +*> set to zero small positive singular values if they are outside +*> specified range. If A .NE. 0 is scaled so that the largest singular +*> value of c*A is around SQRT(BIG), BIG=SLAMCH('O'), then JOBR issues +*> the licence to kill columns of A whose norm in c*A is less than +*> SQRT(SFMIN) (for JOBR = 'R'), or less than SMALL=SFMIN/EPSLN, +*> where SFMIN=SLAMCH('S'), EPSLN=SLAMCH('E'). +*> = 'N': Do not kill small columns of c*A. This option assumes that +*> BLAS and QR factorizations and triangular solvers are +*> implemented to work in that range. If the condition of A +*> is greater than BIG, use CGESVJ. +*> = 'R': RESTRICTED range for sigma(c*A) is [SQRT(SFMIN), SQRT(BIG)] +*> (roughly, as described above). This option is recommended. +*> =========================== +*> For computing the singular values in the FULL range [SFMIN,BIG] +*> use CGESVJ. +*> \endverbatim +*> +*> \param[in] JOBT +*> \verbatim +*> JOBT is CHARACTER*1 +*> If the matrix is square then the procedure may determine to use +*> transposed A if A^* seems to be better with respect to convergence. +*> If the matrix is not square, JOBT is ignored. +*> The decision is based on two values of entropy over the adjoint +*> orbit of A^* * A. See the descriptions of RWORK(6) and RWORK(7). +*> = 'T': transpose if entropy test indicates possibly faster +*> convergence of Jacobi process if A^* is taken as input. If A is +*> replaced with A^*, then the row pivoting is included automatically. +*> = 'N': do not speculate. +*> The option 'T' can be used to compute only the singular values, or +*> the full SVD (U, SIGMA and V). For only one set of singular vectors +*> (U or V), the caller should provide both U and V, as one of the +*> matrices is used as workspace if the matrix A is transposed. +*> The implementer can easily remove this constraint and make the +*> code more complicated. See the descriptions of U and V. +*> In general, this option is considered experimental, and 'N'; should +*> be preferred. This is subject to changes in the future. +*> \endverbatim +*> +*> \param[in] JOBP +*> \verbatim +*> JOBP is CHARACTER*1 +*> Issues the licence to introduce structured perturbations to drown +*> denormalized numbers. This licence should be active if the +*> denormals are poorly implemented, causing slow computation, +*> especially in cases of fast convergence (!). For details see [1,2]. +*> For the sake of simplicity, this perturbations are included only +*> when the full SVD or only the singular values are requested. The +*> implementer/user can easily add the perturbation for the cases of +*> computing one set of singular vectors. +*> = 'P': introduce perturbation +*> = 'N': do not perturb +*> \endverbatim +*> +*> \param[in] M +*> \verbatim +*> M is INTEGER +*> The number of rows of the input matrix A. M >= 0. +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of columns of the input matrix A. M >= N >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is COMPLEX array, dimension (LDA,N) +*> On entry, the M-by-N matrix A. +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,M). +*> \endverbatim +*> +*> \param[out] SVA +*> \verbatim +*> SVA is REAL array, dimension (N) +*> On exit, +*> - For RWORK(1)/RWORK(2) = ONE: The singular values of A. During +*> the computation SVA contains Euclidean column norms of the +*> iterated matrices in the array A. +*> - For RWORK(1) .NE. RWORK(2): The singular values of A are +*> (RWORK(1)/RWORK(2)) * SVA(1:N). This factored form is used if +*> sigma_max(A) overflows or if small singular values have been +*> saved from underflow by scaling the input matrix A. +*> - If JOBR='R' then some of the singular values may be returned +*> as exact zeros obtained by "set to zero" because they are +*> below the numerical rank threshold or are denormalized numbers. +*> \endverbatim +*> +*> \param[out] U +*> \verbatim +*> U is COMPLEX array, dimension ( LDU, N ) or ( LDU, M ) +*> If JOBU = 'U', then U contains on exit the M-by-N matrix of +*> the left singular vectors. +*> If JOBU = 'F', then U contains on exit the M-by-M matrix of +*> the left singular vectors, including an ONB +*> of the orthogonal complement of the Range(A). +*> If JOBU = 'W' .AND. (JOBV = 'V' .AND. JOBT = 'T' .AND. M = N), +*> then U is used as workspace if the procedure +*> replaces A with A^*. In that case, [V] is computed +*> in U as left singular vectors of A^* and then +*> copied back to the V array. This 'W' option is just +*> a reminder to the caller that in this case U is +*> reserved as workspace of length N*N. +*> If JOBU = 'N' U is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDU +*> \verbatim +*> LDU is INTEGER +*> The leading dimension of the array U, LDU >= 1. +*> IF JOBU = 'U' or 'F' or 'W', then LDU >= M. +*> \endverbatim +*> +*> \param[out] V +*> \verbatim +*> V is COMPLEX array, dimension ( LDV, N ) +*> If JOBV = 'V', 'J' then V contains on exit the N-by-N matrix of +*> the right singular vectors; +*> If JOBV = 'W', AND (JOBU = 'U' AND JOBT = 'T' AND M = N), +*> then V is used as workspace if the pprocedure +*> replaces A with A^*. In that case, [U] is computed +*> in V as right singular vectors of A^* and then +*> copied back to the U array. This 'W' option is just +*> a reminder to the caller that in this case V is +*> reserved as workspace of length N*N. +*> If JOBV = 'N' V is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDV +*> \verbatim +*> LDV is INTEGER +*> The leading dimension of the array V, LDV >= 1. +*> If JOBV = 'V' or 'J' or 'W', then LDV >= N. +*> \endverbatim +*> +*> \param[out] CWORK +*> \verbatim +*> CWORK is COMPLEX array, dimension (MAX(2,LWORK)) +*> If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit CWORK(1) contains the required length of +*> CWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[in] LWORK +*> \verbatim +*> LWORK is INTEGER +*> Length of CWORK to confirm proper allocation of workspace. +*> LWORK depends on the job: +*> +*> 1. If only SIGMA is needed ( JOBU = 'N', JOBV = 'N' ) and +*> 1.1 .. no scaled condition estimate required (JOBA.NE.'E'.AND.JOBA.NE.'G'): +*> LWORK >= 2*N+1. This is the minimal requirement. +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= N + (N+1)*NB. Here NB is the optimal +*> block size for CGEQP3 and CGEQRF. +*> In general, optimal LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CGEQRF), LWORK(CGESVJ)). +*> 1.2. .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). In this case, LWORK the minimal +*> requirement is LWORK >= N*N + 2*N. +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= max(N+(N+1)*NB, N*N+2*N)=N**2+2*N. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CGEQRF), LWORK(CGESVJ), +*> N*N+LWORK(CPOCON)). +*> 2. If SIGMA and the right singular vectors are needed (JOBV = 'V'), +*> (JOBU = 'N') +*> 2.1 .. no scaled condition estimate requested (JOBE = 'N'): +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance, +*> LWORK >= max(N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for CGEQP3, CGEQRF, CGELQF, +*> CUNMLQ. In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3), N+LWORK(CGESVJ), +*> N+LWORK(CGELQF), 2*N+LWORK(CGEQRF), N+LWORK(CUNMLQ)). +*> 2.2 .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance, +*> LWORK >= max(N+(N+1)*NB, 2*N,2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for CGEQP3, CGEQRF, CGELQF, +*> CUNMLQ. In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3), LWORK(CPOCON), N+LWORK(CGESVJ), +*> N+LWORK(CGELQF), 2*N+LWORK(CGEQRF), N+LWORK(CUNMLQ)). +*> 3. If SIGMA and the left singular vectors are needed +*> 3.1 .. no scaled condition estimate requested (JOBE = 'N'): +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance: +*> if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for CGEQP3, CGEQRF, CUNMQR. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3), 2*N+LWORK(CGEQRF), N+LWORK(CUNMQR)). +*> 3.2 .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance: +*> if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for CGEQP3, CGEQRF, CUNMQR. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(CGEQP3),N+LWORK(CPOCON), +*> 2*N+LWORK(CGEQRF), N+LWORK(CUNMQR)). +*> +*> 4. If the full SVD is needed: (JOBU = 'U' or JOBU = 'F') and +*> 4.1. if JOBV = 'V' +*> the minimal requirement is LWORK >= 5*N+2*N*N. +*> 4.2. if JOBV = 'J' the minimal requirement is +*> LWORK >= 4*N+N*N. +*> In both cases, the allocated CWORK can accommodate blocked runs +*> of CGEQP3, CGEQRF, CGELQF, CUNMQR, CUNMLQ. +*> +*> If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit CWORK(1) contains the optimal and CWORK(2) contains the +*> minimal length of CWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is REAL array, dimension (MAX(7,LRWORK)) +*> On exit, +*> RWORK(1) = Determines the scaling factor SCALE = RWORK(2) / RWORK(1) +*> such that SCALE*SVA(1:N) are the computed singular values +*> of A. (See the description of SVA().) +*> RWORK(2) = See the description of RWORK(1). +*> RWORK(3) = SCONDA is an estimate for the condition number of +*> column equilibrated A. (If JOBA = 'E' or 'G') +*> SCONDA is an estimate of SQRT(||(R^* * R)^(-1)||_1). +*> It is computed using CPOCON. It holds +*> N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA +*> where R is the triangular factor from the QRF of A. +*> However, if R is truncated and the numerical rank is +*> determined to be strictly smaller than N, SCONDA is +*> returned as -1, thus indicating that the smallest +*> singular values might be lost. +*> +*> If full SVD is needed, the following two condition numbers are +*> useful for the analysis of the algorithm. They are provided for +*> a developer/implementer who is familiar with the details of +*> the method. +*> +*> RWORK(4) = an estimate of the scaled condition number of the +*> triangular factor in the first QR factorization. +*> RWORK(5) = an estimate of the scaled condition number of the +*> triangular factor in the second QR factorization. +*> The following two parameters are computed if JOBT = 'T'. +*> They are provided for a developer/implementer who is familiar +*> with the details of the method. +*> RWORK(6) = the entropy of A^* * A :: this is the Shannon entropy +*> of diag(A^* * A) / Trace(A^* * A) taken as point in the +*> probability simplex. +*> RWORK(7) = the entropy of A * A^*. (See the description of RWORK(6).) +*> If the call to CGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit RWORK(1) contains the required length of +*> RWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[in] LRWORK +*> \verbatim +*> LRWORK is INTEGER +*> Length of RWORK to confirm proper allocation of workspace. +*> LRWORK depends on the job: +*> +*> 1. If only the singular values are requested i.e. if +*> LSAME(JOBU,'N') .AND. LSAME(JOBV,'N') +*> then: +*> 1.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then: LRWORK = max( 7, 2 * M ). +*> 1.2. Otherwise, LRWORK = max( 7, N ). +*> 2. If singular values with the right singular vectors are requested +*> i.e. if +*> (LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) .AND. +*> .NOT.(LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) +*> then: +*> 2.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 2.2. Otherwise, LRWORK = max( 7, N ). +*> 3. If singular values with the left singular vectors are requested, i.e. if +*> (LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND. +*> .NOT.(LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) +*> then: +*> 3.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 3.2. Otherwise, LRWORK = max( 7, N ). +*> 4. If singular values with both the left and the right singular vectors +*> are requested, i.e. if +*> (LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND. +*> (LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) +*> then: +*> 4.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 4.2. Otherwise, LRWORK = max( 7, N ). +*> +*> If, on entry, LRWORK = -1 or LWORK=-1, a workspace query is assumed and +*> the length of RWORK is returned in RWORK(1). +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, of dimension at least 4, that further depends +*> on the job: +*> +*> 1. If only the singular values are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 2. If the singular values and the right singular vectors are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 3. If the singular values and the left singular vectors are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 4. If the singular values with both the left and the right singular vectors +*> are requested, then: +*> 4.1. If LSAME(JOBV,'J') the length of IWORK is determined as follows: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 4.2. If LSAME(JOBV,'V') the length of IWORK is determined as follows: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is 2*N+M; otherwise the length of IWORK is 2*N. +*> +*> On exit, +*> IWORK(1) = the numerical rank determined after the initial +*> QR factorization with pivoting. See the descriptions +*> of JOBA and JOBR. +*> IWORK(2) = the number of the computed nonzero singular values +*> IWORK(3) = if nonzero, a warning message: +*> If IWORK(3) = 1 then some of the column norms of A +*> were denormalized floats. The requested high accuracy +*> is not warranted by the data. +*> IWORK(4) = 1 or -1. If IWORK(4) = 1, then the procedure used A^* to +*> do the job as specified by the JOB parameters. +*> If the call to CGEJSV is a workspace query (indicated by LWORK = -1 and +*> LRWORK = -1), then on exit IWORK(1) contains the required length of +*> IWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> < 0: if INFO = -i, then the i-th argument had an illegal value. +*> = 0: successful exit; +*> > 0: CGEJSV did not converge in the maximal allowed number +*> of sweeps. The computed values may be inaccurate. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complexGEsing +* +*> \par Further Details: +* ===================== +*> +*> \verbatim +*> CGEJSV implements a preconditioned Jacobi SVD algorithm. It uses CGEQP3, +*> CGEQRF, and CGELQF as preprocessors and preconditioners. Optionally, an +*> additional row pivoting can be used as a preprocessor, which in some +*> cases results in much higher accuracy. An example is matrix A with the +*> structure A = D1 * C * D2, where D1, D2 are arbitrarily ill-conditioned +*> diagonal matrices and C is well-conditioned matrix. In that case, complete +*> pivoting in the first QR factorizations provides accuracy dependent on the +*> condition number of C, and independent of D1, D2. Such higher accuracy is +*> not completely understood theoretically, but it works well in practice. +*> Further, if A can be written as A = B*D, with well-conditioned B and some +*> diagonal D, then the high accuracy is guaranteed, both theoretically and +*> in software, independent of D. For more details see [1], [2]. +*> The computational range for the singular values can be the full range +*> ( UNDERFLOW,OVERFLOW ), provided that the machine arithmetic and the BLAS +*> & LAPACK routines called by CGEJSV are implemented to work in that range. +*> If that is not the case, then the restriction for safe computation with +*> the singular values in the range of normalized IEEE numbers is that the +*> spectral condition number kappa(A)=sigma_max(A)/sigma_min(A) does not +*> overflow. This code (CGEJSV) is best used in this restricted range, +*> meaning that singular values of magnitude below ||A||_2 / SLAMCH('O') are +*> returned as zeros. See JOBR for details on this. +*> Further, this implementation is somewhat slower than the one described +*> in [1,2] due to replacement of some non-LAPACK components, and because +*> the choice of some tuning parameters in the iterative part (CGESVJ) is +*> left to the implementer on a particular machine. +*> The rank revealing QR factorization (in this code: CGEQP3) should be +*> implemented as in [3]. We have a new version of CGEQP3 under development +*> that is more robust than the current one in LAPACK, with a cleaner cut in +*> rank deficient cases. It will be available in the SIGMA library [4]. +*> If M is much larger than N, it is obvious that the initial QRF with +*> column pivoting can be preprocessed by the QRF without pivoting. That +*> well known trick is not used in CGEJSV because in some cases heavy row +*> weighting can be treated with complete pivoting. The overhead in cases +*> M much larger than N is then only due to pivoting, but the benefits in +*> terms of accuracy have prevailed. The implementer/user can incorporate +*> this extra QRF step easily. The implementer can also improve data movement +*> (matrix transpose, matrix copy, matrix transposed copy) - this +*> implementation of CGEJSV uses only the simplest, naive data movement. +*> \endverbatim +* +*> \par Contributor: +* ================== +*> +*> Zlatko Drmac (Zagreb, Croatia) +* +*> \par References: +* ================ +*> +*> \verbatim +*> +*> [1] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. +*> LAPACK Working note 169. +*> [2] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. +*> LAPACK Working note 170. +*> [3] Z. Drmac and Z. Bujanovic: On the failure of rank-revealing QR +*> factorization software - a case study. +*> ACM Trans. Math. Softw. Vol. 35, No 2 (2008), pp. 1-28. +*> LAPACK Working note 176. +*> [4] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, +*> QSVD, (H,K)-SVD computations. +*> Department of Mathematics, University of Zagreb, 2008, 2016. +*> \endverbatim +* +*> \par Bugs, examples and comments: +* ================================= +*> +*> Please report all bugs and send interesting examples and/or comments to +*> drmac@math.hr. Thank you. +*> +* ===================================================================== + SUBROUTINE CGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, + $ M, N, A, LDA, SVA, U, LDU, V, LDV, + $ CWORK, LWORK, RWORK, LRWORK, IWORK, INFO ) +* +* -- LAPACK computational routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + IMPLICIT NONE + INTEGER INFO, LDA, LDU, LDV, LWORK, LRWORK, M, N +* .. +* .. Array Arguments .. + COMPLEX A( LDA, * ), U( LDU, * ), V( LDV, * ), CWORK( LWORK ) + REAL SVA( N ), RWORK( LRWORK ) + INTEGER IWORK( * ) + CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* =========================================================================== +* +* .. Local Parameters .. + REAL ZERO, ONE + PARAMETER ( ZERO = 0.0E0, ONE = 1.0E0 ) + COMPLEX CZERO, CONE + PARAMETER ( CZERO = ( 0.0E0, 0.0E0 ), CONE = ( 1.0E0, 0.0E0 ) ) +* .. +* .. Local Scalars .. + COMPLEX CTEMP + REAL AAPP, AAQQ, AATMAX, AATMIN, BIG, BIG1, COND_OK, + $ CONDR1, CONDR2, ENTRA, ENTRAT, EPSLN, MAXPRJ, SCALEM, + $ SCONDA, SFMIN, SMALL, TEMP1, USCAL1, USCAL2, XSC + INTEGER IERR, N1, NR, NUMRANK, p, q, WARNING + LOGICAL ALMORT, DEFR, ERREST, GOSCAL, JRACC, KILL, LQUERY, + $ LSVEC, L2ABER, L2KILL, L2PERT, L2RANK, L2TRAN, NOSCAL, + $ ROWPIV, RSVEC, TRANSP +* + INTEGER OPTWRK, MINWRK, MINRWRK, MINIWRK + INTEGER LWCON, LWLQF, LWQP3, LWQRF, LWUNMLQ, LWUNMQR, LWUNMQRM, + $ LWSVDJ, LWSVDJV, LRWQP3, LRWCON, LRWSVDJ, IWOFF + INTEGER LWRK_CGELQF, LWRK_CGEQP3, LWRK_CGEQP3N, LWRK_CGEQRF, + $ LWRK_CGESVJ, LWRK_CGESVJV, LWRK_CGESVJU, LWRK_CUNMLQ, + $ LWRK_CUNMQR, LWRK_CUNMQRM +* .. +* .. Local Arrays + COMPLEX CDUMMY(1) + REAL RDUMMY(1) +* +* .. Intrinsic Functions .. + INTRINSIC ABS, CMPLX, CONJG, ALOG, MAX, MIN, REAL, NINT, SQRT +* .. +* .. External Functions .. + REAL SLAMCH, SCNRM2 + INTEGER ISAMAX, ICAMAX + LOGICAL LSAME + EXTERNAL ISAMAX, ICAMAX, LSAME, SLAMCH, SCNRM2 +* .. +* .. External Subroutines .. + EXTERNAL SLASSQ, CCOPY, CGELQF, CGEQP3, CGEQRF, CLACPY, CLAPMR, + $ CLASCL, SLASCL, CLASET, CLASSQ, CLASWP, CUNGQR, CUNMLQ, + $ CUNMQR, CPOCON, SSCAL, CSSCAL, CSWAP, CTRSM, CLACGV, + $ XERBLA +* + EXTERNAL CGESVJ +* .. +* +* Test the input arguments +* + LSVEC = LSAME( JOBU, 'U' ) .OR. LSAME( JOBU, 'F' ) + JRACC = LSAME( JOBV, 'J' ) + RSVEC = LSAME( JOBV, 'V' ) .OR. JRACC + ROWPIV = LSAME( JOBA, 'F' ) .OR. LSAME( JOBA, 'G' ) + L2RANK = LSAME( JOBA, 'R' ) + L2ABER = LSAME( JOBA, 'A' ) + ERREST = LSAME( JOBA, 'E' ) .OR. LSAME( JOBA, 'G' ) + L2TRAN = LSAME( JOBT, 'T' ) .AND. ( M .EQ. N ) + L2KILL = LSAME( JOBR, 'R' ) + DEFR = LSAME( JOBR, 'N' ) + L2PERT = LSAME( JOBP, 'P' ) +* + LQUERY = ( LWORK .EQ. -1 ) .OR. ( LRWORK .EQ. -1 ) +* + IF ( .NOT.(ROWPIV .OR. L2RANK .OR. L2ABER .OR. + $ ERREST .OR. LSAME( JOBA, 'C' ) )) THEN + INFO = - 1 + ELSE IF ( .NOT.( LSVEC .OR. LSAME( JOBU, 'N' ) .OR. + $ ( LSAME( JOBU, 'W' ) .AND. RSVEC .AND. L2TRAN ) ) ) THEN + INFO = - 2 + ELSE IF ( .NOT.( RSVEC .OR. LSAME( JOBV, 'N' ) .OR. + $ ( LSAME( JOBV, 'W' ) .AND. LSVEC .AND. L2TRAN ) ) ) THEN + INFO = - 3 + ELSE IF ( .NOT. ( L2KILL .OR. DEFR ) ) THEN + INFO = - 4 + ELSE IF ( .NOT. ( LSAME(JOBT,'T') .OR. LSAME(JOBT,'N') ) ) THEN + INFO = - 5 + ELSE IF ( .NOT. ( L2PERT .OR. LSAME( JOBP, 'N' ) ) ) THEN + INFO = - 6 + ELSE IF ( M .LT. 0 ) THEN + INFO = - 7 + ELSE IF ( ( N .LT. 0 ) .OR. ( N .GT. M ) ) THEN + INFO = - 8 + ELSE IF ( LDA .LT. M ) THEN + INFO = - 10 + ELSE IF ( LSVEC .AND. ( LDU .LT. M ) ) THEN + INFO = - 13 + ELSE IF ( RSVEC .AND. ( LDV .LT. N ) ) THEN + INFO = - 15 + ELSE +* #:) + INFO = 0 + END IF +* + IF ( INFO .EQ. 0 ) THEN +* .. compute the minimal and the optimal workspace lengths +* [[The expressions for computing the minimal and the optimal +* values of LCWORK, LRWORK are written with a lot of redundancy and +* can be simplified. However, this verbose form is useful for +* maintenance and modifications of the code.]] +* +* .. minimal workspace length for CGEQP3 of an M x N matrix, +* CGEQRF of an N x N matrix, CGELQF of an N x N matrix, +* CUNMLQ for computing N x N matrix, CUNMQR for computing N x N +* matrix, CUNMQR for computing M x N matrix, respectively. + LWQP3 = N+1 + LWQRF = MAX( 1, N ) + LWLQF = MAX( 1, N ) + LWUNMLQ = MAX( 1, N ) + LWUNMQR = MAX( 1, N ) + LWUNMQRM = MAX( 1, M ) +* .. minimal workspace length for CPOCON of an N x N matrix + LWCON = 2 * N +* .. minimal workspace length for CGESVJ of an N x N matrix, +* without and with explicit accumulation of Jacobi rotations + LWSVDJ = MAX( 2 * N, 1 ) + LWSVDJV = MAX( 2 * N, 1 ) +* .. minimal REAL workspace length for CGEQP3, CPOCON, CGESVJ + LRWQP3 = 2 * N + LRWCON = N + LRWSVDJ = N + IF ( LQUERY ) THEN + CALL CGEQP3( M, N, A, LDA, IWORK, CDUMMY, CDUMMY, -1, + $ RDUMMY, IERR ) + LWRK_CGEQP3 = INT( CDUMMY(1) ) + CALL CGEQRF( N, N, A, LDA, CDUMMY, CDUMMY,-1, IERR ) + LWRK_CGEQRF = INT( CDUMMY(1) ) + CALL CGELQF( N, N, A, LDA, CDUMMY, CDUMMY,-1, IERR ) + LWRK_CGELQF = INT( CDUMMY(1) ) + END IF + MINWRK = 2 + OPTWRK = 2 + MINIWRK = N + IF ( .NOT. (LSVEC .OR. RSVEC ) ) THEN +* .. minimal and optimal sizes of the complex workspace if +* only the singular values are requested + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N**2+LWCON, N+LWQRF, LWSVDJ ) + ELSE + MINWRK = MAX( N+LWQP3, N+LWQRF, LWSVDJ ) + END IF + IF ( LQUERY ) THEN + CALL CGESVJ( 'L', 'N', 'N', N, N, A, LDA, SVA, N, V, + $ LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_CGEQP3, N**2+LWCON, + $ N+LWRK_CGEQRF, LWRK_CGESVJ ) + ELSE + OPTWRK = MAX( N+LWRK_CGEQP3, N+LWRK_CGEQRF, + $ LWRK_CGESVJ ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWCON, LRWSVDJ ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWCON, LRWSVDJ ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE IF ( RSVEC .AND. (.NOT.LSVEC) ) THEN +* .. minimal and optimal sizes of the complex workspace if the +* singular values and the right singular vectors are requested + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, LWCON, LWSVDJ, N+LWLQF, + $ 2*N+LWQRF, N+LWSVDJ, N+LWUNMLQ ) + ELSE + MINWRK = MAX( N+LWQP3, LWSVDJ, N+LWLQF, 2*N+LWQRF, + $ N+LWSVDJ, N+LWUNMLQ ) + END IF + IF ( LQUERY ) THEN + CALL CGESVJ( 'L', 'U', 'N', N,N, U, LDU, SVA, N, A, + $ LDA, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJ = INT( CDUMMY(1) ) + CALL CUNMLQ( 'L', 'C', N, N, N, A, LDA, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_CUNMLQ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_CGEQP3, LWCON, LWRK_CGESVJ, + $ N+LWRK_CGELQF, 2*N+LWRK_CGEQRF, + $ N+LWRK_CGESVJ, N+LWRK_CUNMLQ ) + ELSE + OPTWRK = MAX( N+LWRK_CGEQP3, LWRK_CGESVJ,N+LWRK_CGELQF, + $ 2*N+LWRK_CGEQRF, N+LWRK_CGESVJ, + $ N+LWRK_CUNMLQ ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE IF ( LSVEC .AND. (.NOT.RSVEC) ) THEN +* .. minimal and optimal sizes of the complex workspace if the +* singular values and the left singular vectors are requested + IF ( ERREST ) THEN + MINWRK = N + MAX( LWQP3,LWCON,N+LWQRF,LWSVDJ,LWUNMQRM ) + ELSE + MINWRK = N + MAX( LWQP3, N+LWQRF, LWSVDJ, LWUNMQRM ) + END IF + IF ( LQUERY ) THEN + CALL CGESVJ( 'L', 'U', 'N', N,N, U, LDU, SVA, N, A, + $ LDA, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJ = INT( CDUMMY(1) ) + CALL CUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_CUNMQRM = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = N + MAX( LWRK_CGEQP3, LWCON, N+LWRK_CGEQRF, + $ LWRK_CGESVJ, LWRK_CUNMQRM ) + ELSE + OPTWRK = N + MAX( LWRK_CGEQP3, N+LWRK_CGEQRF, + $ LWRK_CGESVJ, LWRK_CUNMQRM ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE +* .. minimal and optimal sizes of the complex workspace if the +* full SVD is requested + IF ( .NOT. JRACC ) THEN + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N+LWCON, 2*N+N**2+LWCON, + $ 2*N+LWQRF, 2*N+LWQP3, + $ 2*N+N**2+N+LWLQF, 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWSVDJ, 2*N+N**2+N+LWSVDJV, + $ 2*N+N**2+N+LWUNMQR,2*N+N**2+N+LWUNMLQ, + $ N+N**2+LWSVDJ, N+LWUNMQRM ) + ELSE + MINWRK = MAX( N+LWQP3, 2*N+N**2+LWCON, + $ 2*N+LWQRF, 2*N+LWQP3, + $ 2*N+N**2+N+LWLQF, 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWSVDJ, 2*N+N**2+N+LWSVDJV, + $ 2*N+N**2+N+LWUNMQR,2*N+N**2+N+LWUNMLQ, + $ N+N**2+LWSVDJ, N+LWUNMQRM ) + END IF + MINIWRK = MINIWRK + N + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N+LWCON, 2*N+LWQRF, + $ 2*N+N**2+LWSVDJV, 2*N+N**2+N+LWUNMQR, + $ N+LWUNMQRM ) + ELSE + MINWRK = MAX( N+LWQP3, 2*N+LWQRF, + $ 2*N+N**2+LWSVDJV, 2*N+N**2+N+LWUNMQR, + $ N+LWUNMQRM ) + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + END IF + IF ( LQUERY ) THEN + CALL CUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_CUNMQRM = INT( CDUMMY(1) ) + CALL CUNMQR( 'L', 'N', N, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_CUNMQR = INT( CDUMMY(1) ) + IF ( .NOT. JRACC ) THEN + CALL CGEQP3( N,N, A, LDA, IWORK, CDUMMY,CDUMMY, -1, + $ RDUMMY, IERR ) + LWRK_CGEQP3N = INT( CDUMMY(1) ) + CALL CGESVJ( 'L', 'U', 'N', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJ = INT( CDUMMY(1) ) + CALL CGESVJ( 'U', 'U', 'N', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJU = INT( CDUMMY(1) ) + CALL CGESVJ( 'L', 'U', 'V', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJV = INT( CDUMMY(1) ) + CALL CUNMLQ( 'L', 'C', N, N, N, A, LDA, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_CUNMLQ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_CGEQP3, N+LWCON, + $ 2*N+N**2+LWCON, 2*N+LWRK_CGEQRF, + $ 2*N+LWRK_CGEQP3N, + $ 2*N+N**2+N+LWRK_CGELQF, + $ 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWRK_CGESVJ, + $ 2*N+N**2+N+LWRK_CGESVJV, + $ 2*N+N**2+N+LWRK_CUNMQR, + $ 2*N+N**2+N+LWRK_CUNMLQ, + $ N+N**2+LWRK_CGESVJU, + $ N+LWRK_CUNMQRM ) + ELSE + OPTWRK = MAX( N+LWRK_CGEQP3, + $ 2*N+N**2+LWCON, 2*N+LWRK_CGEQRF, + $ 2*N+LWRK_CGEQP3N, + $ 2*N+N**2+N+LWRK_CGELQF, + $ 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWRK_CGESVJ, + $ 2*N+N**2+N+LWRK_CGESVJV, + $ 2*N+N**2+N+LWRK_CUNMQR, + $ 2*N+N**2+N+LWRK_CUNMLQ, + $ N+N**2+LWRK_CGESVJU, + $ N+LWRK_CUNMQRM ) + END IF + ELSE + CALL CGESVJ( 'L', 'U', 'V', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_CGESVJV = INT( CDUMMY(1) ) + CALL CUNMQR( 'L', 'N', N, N, N, CDUMMY, N, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_CUNMQR = INT( CDUMMY(1) ) + CALL CUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_CUNMQRM = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_CGEQP3, N+LWCON, + $ 2*N+LWRK_CGEQRF, 2*N+N**2, + $ 2*N+N**2+LWRK_CGESVJV, + $ 2*N+N**2+N+LWRK_CUNMQR,N+LWRK_CUNMQRM ) + ELSE + OPTWRK = MAX( N+LWRK_CGEQP3, 2*N+LWRK_CGEQRF, + $ 2*N+N**2, 2*N+N**2+LWRK_CGESVJV, + $ 2*N+N**2+N+LWRK_CUNMQR, + $ N+LWRK_CUNMQRM ) + END IF + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + END IF + END IF + MINWRK = MAX( 2, MINWRK ) + OPTWRK = MAX( OPTWRK, MINWRK ) + IF ( LWORK .LT. MINWRK .AND. (.NOT.LQUERY) ) INFO = - 17 + IF ( LRWORK .LT. MINRWRK .AND. (.NOT.LQUERY) ) INFO = - 19 + END IF +* + IF ( INFO .NE. 0 ) THEN +* #:( + CALL XERBLA( 'CGEJSV', - INFO ) + RETURN + ELSE IF ( LQUERY ) THEN + CWORK(1) = OPTWRK + CWORK(2) = MINWRK + RWORK(1) = MINRWRK + IWORK(1) = MAX( 4, MINIWRK ) + RETURN + END IF +* +* Quick return for void matrix (Y3K safe) +* #:) + IF ( ( M .EQ. 0 ) .OR. ( N .EQ. 0 ) ) THEN + IWORK(1:4) = 0 + RWORK(1:7) = 0 + RETURN + ENDIF +* +* Determine whether the matrix U should be M x N or M x M +* + IF ( LSVEC ) THEN + N1 = N + IF ( LSAME( JOBU, 'F' ) ) N1 = M + END IF +* +* Set numerical parameters +* +*! NOTE: Make sure SLAMCH() does not fail on the target architecture. +* + EPSLN = SLAMCH('Epsilon') + SFMIN = SLAMCH('SafeMinimum') + SMALL = SFMIN / EPSLN + BIG = SLAMCH('O') +* BIG = ONE / SFMIN +* +* Initialize SVA(1:N) = diag( ||A e_i||_2 )_1^N +* +*(!) If necessary, scale SVA() to protect the largest norm from +* overflow. It is possible that this scaling pushes the smallest +* column norm left from the underflow threshold (extreme case). +* + SCALEM = ONE / SQRT(REAL(M)*REAL(N)) + NOSCAL = .TRUE. + GOSCAL = .TRUE. + DO 1874 p = 1, N + AAPP = ZERO + AAQQ = ONE + CALL CLASSQ( M, A(1,p), 1, AAPP, AAQQ ) + IF ( AAPP .GT. BIG ) THEN + INFO = - 9 + CALL XERBLA( 'CGEJSV', -INFO ) + RETURN + END IF + AAQQ = SQRT(AAQQ) + IF ( ( AAPP .LT. (BIG / AAQQ) ) .AND. NOSCAL ) THEN + SVA(p) = AAPP * AAQQ + ELSE + NOSCAL = .FALSE. + SVA(p) = AAPP * ( AAQQ * SCALEM ) + IF ( GOSCAL ) THEN + GOSCAL = .FALSE. + CALL SSCAL( p-1, SCALEM, SVA, 1 ) + END IF + END IF + 1874 CONTINUE +* + IF ( NOSCAL ) SCALEM = ONE +* + AAPP = ZERO + AAQQ = BIG + DO 4781 p = 1, N + AAPP = MAX( AAPP, SVA(p) ) + IF ( SVA(p) .NE. ZERO ) AAQQ = MIN( AAQQ, SVA(p) ) + 4781 CONTINUE +* +* Quick return for zero M x N matrix +* #:) + IF ( AAPP .EQ. ZERO ) THEN + IF ( LSVEC ) CALL CLASET( 'G', M, N1, CZERO, CONE, U, LDU ) + IF ( RSVEC ) CALL CLASET( 'G', N, N, CZERO, CONE, V, LDV ) + RWORK(1) = ONE + RWORK(2) = ONE + IF ( ERREST ) RWORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = ONE + RWORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ZERO + RWORK(7) = ZERO + END IF + IWORK(1) = 0 + IWORK(2) = 0 + IWORK(3) = 0 + IWORK(4) = -1 + RETURN + END IF +* +* Issue warning if denormalized column norms detected. Override the +* high relative accuracy request. Issue licence to kill nonzero columns +* (set them to zero) whose norm is less than sigma_max / BIG (roughly). +* #:( + WARNING = 0 + IF ( AAQQ .LE. SFMIN ) THEN + L2RANK = .TRUE. + L2KILL = .TRUE. + WARNING = 1 + END IF +* +* Quick return for one-column matrix +* #:) + IF ( N .EQ. 1 ) THEN +* + IF ( LSVEC ) THEN + CALL CLASCL( 'G',0,0,SVA(1),SCALEM, M,1,A(1,1),LDA,IERR ) + CALL CLACPY( 'A', M, 1, A, LDA, U, LDU ) +* computing all M left singular vectors of the M x 1 matrix + IF ( N1 .NE. N ) THEN + CALL CGEQRF( M, N, U,LDU, CWORK, CWORK(N+1),LWORK-N,IERR ) + CALL CUNGQR( M,N1,1, U,LDU,CWORK,CWORK(N+1),LWORK-N,IERR ) + CALL CCOPY( M, A(1,1), 1, U(1,1), 1 ) + END IF + END IF + IF ( RSVEC ) THEN + V(1,1) = CONE + END IF + IF ( SVA(1) .LT. (BIG*SCALEM) ) THEN + SVA(1) = SVA(1) / SCALEM + SCALEM = ONE + END IF + RWORK(1) = ONE / SCALEM + RWORK(2) = ONE + IF ( SVA(1) .NE. ZERO ) THEN + IWORK(1) = 1 + IF ( ( SVA(1) / SCALEM) .GE. SFMIN ) THEN + IWORK(2) = 1 + ELSE + IWORK(2) = 0 + END IF + ELSE + IWORK(1) = 0 + IWORK(2) = 0 + END IF + IWORK(3) = 0 + IWORK(4) = -1 + IF ( ERREST ) RWORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = ONE + RWORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ZERO + RWORK(7) = ZERO + END IF + RETURN +* + END IF +* + TRANSP = .FALSE. +* + AATMAX = -ONE + AATMIN = BIG + IF ( ROWPIV .OR. L2TRAN ) THEN +* +* Compute the row norms, needed to determine row pivoting sequence +* (in the case of heavily row weighted A, row pivoting is strongly +* advised) and to collect information needed to compare the +* structures of A * A^* and A^* * A (in the case L2TRAN.EQ..TRUE.). +* + IF ( L2TRAN ) THEN + DO 1950 p = 1, M + XSC = ZERO + TEMP1 = ONE + CALL CLASSQ( N, A(p,1), LDA, XSC, TEMP1 ) +* CLASSQ gets both the ell_2 and the ell_infinity norm +* in one pass through the vector + RWORK(M+p) = XSC * SCALEM + RWORK(p) = XSC * (SCALEM*SQRT(TEMP1)) + AATMAX = MAX( AATMAX, RWORK(p) ) + IF (RWORK(p) .NE. ZERO) + $ AATMIN = MIN(AATMIN,RWORK(p)) + 1950 CONTINUE + ELSE + DO 1904 p = 1, M + RWORK(M+p) = SCALEM*ABS( A(p,ICAMAX(N,A(p,1),LDA)) ) + AATMAX = MAX( AATMAX, RWORK(M+p) ) + AATMIN = MIN( AATMIN, RWORK(M+p) ) + 1904 CONTINUE + END IF +* + END IF +* +* For square matrix A try to determine whether A^* would be better +* input for the preconditioned Jacobi SVD, with faster convergence. +* The decision is based on an O(N) function of the vector of column +* and row norms of A, based on the Shannon entropy. This should give +* the right choice in most cases when the difference actually matters. +* It may fail and pick the slower converging side. +* + ENTRA = ZERO + ENTRAT = ZERO + IF ( L2TRAN ) THEN +* + XSC = ZERO + TEMP1 = ONE + CALL SLASSQ( N, SVA, 1, XSC, TEMP1 ) + TEMP1 = ONE / TEMP1 +* + ENTRA = ZERO + DO 1113 p = 1, N + BIG1 = ( ( SVA(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRA = ENTRA + BIG1 * ALOG(BIG1) + 1113 CONTINUE + ENTRA = - ENTRA / ALOG(REAL(N)) +* +* Now, SVA().^2/Trace(A^* * A) is a point in the probability simplex. +* It is derived from the diagonal of A^* * A. Do the same with the +* diagonal of A * A^*, compute the entropy of the corresponding +* probability distribution. Note that A * A^* and A^* * A have the +* same trace. +* + ENTRAT = ZERO + DO 1114 p = 1, M + BIG1 = ( ( RWORK(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRAT = ENTRAT + BIG1 * ALOG(BIG1) + 1114 CONTINUE + ENTRAT = - ENTRAT / ALOG(REAL(M)) +* +* Analyze the entropies and decide A or A^*. Smaller entropy +* usually means better input for the algorithm. +* + TRANSP = ( ENTRAT .LT. ENTRA ) +* +* If A^* is better than A, take the adjoint of A. This is allowed +* only for square matrices, M=N. + IF ( TRANSP ) THEN +* In an optimal implementation, this trivial transpose +* should be replaced with faster transpose. + DO 1115 p = 1, N - 1 + A(p,p) = CONJG(A(p,p)) + DO 1116 q = p + 1, N + CTEMP = CONJG(A(q,p)) + A(q,p) = CONJG(A(p,q)) + A(p,q) = CTEMP + 1116 CONTINUE + 1115 CONTINUE + A(N,N) = CONJG(A(N,N)) + DO 1117 p = 1, N + RWORK(M+p) = SVA(p) + SVA(p) = RWORK(p) +* previously computed row 2-norms are now column 2-norms +* of the transposed matrix + 1117 CONTINUE + TEMP1 = AAPP + AAPP = AATMAX + AATMAX = TEMP1 + TEMP1 = AAQQ + AAQQ = AATMIN + AATMIN = TEMP1 + KILL = LSVEC + LSVEC = RSVEC + RSVEC = KILL + IF ( LSVEC ) N1 = N +* + ROWPIV = .TRUE. + END IF +* + END IF +* END IF L2TRAN +* +* Scale the matrix so that its maximal singular value remains less +* than SQRT(BIG) -- the matrix is scaled so that its maximal column +* has Euclidean norm equal to SQRT(BIG/N). The only reason to keep +* SQRT(BIG) instead of BIG is the fact that CGEJSV uses LAPACK and +* BLAS routines that, in some implementations, are not capable of +* working in the full interval [SFMIN,BIG] and that they may provoke +* overflows in the intermediate results. If the singular values spread +* from SFMIN to BIG, then CGESVJ will compute them. So, in that case, +* one should use CGESVJ instead of CGEJSV. + BIG1 = SQRT( BIG ) + TEMP1 = SQRT( BIG / REAL(N) ) +* >> for future updates: allow bigger range, i.e. the largest column +* will be allowed up to BIG/N and CGESVJ will do the rest. However, for +* this all other (LAPACK) components must allow such a range. +* TEMP1 = BIG/REAL(N) +* TEMP1 = BIG * EPSLN this should 'almost' work with current LAPACK components + CALL SLASCL( 'G', 0, 0, AAPP, TEMP1, N, 1, SVA, N, IERR ) + IF ( AAQQ .GT. (AAPP * SFMIN) ) THEN + AAQQ = ( AAQQ / AAPP ) * TEMP1 + ELSE + AAQQ = ( AAQQ * TEMP1 ) / AAPP + END IF + TEMP1 = TEMP1 * SCALEM + CALL CLASCL( 'G', 0, 0, AAPP, TEMP1, M, N, A, LDA, IERR ) +* +* To undo scaling at the end of this procedure, multiply the +* computed singular values with USCAL2 / USCAL1. +* + USCAL1 = TEMP1 + USCAL2 = AAPP +* + IF ( L2KILL ) THEN +* L2KILL enforces computation of nonzero singular values in +* the restricted range of condition number of the initial A, +* sigma_max(A) / sigma_min(A) approx. SQRT(BIG)/SQRT(SFMIN). + XSC = SQRT( SFMIN ) + ELSE + XSC = SMALL +* +* Now, if the condition number of A is too big, +* sigma_max(A) / sigma_min(A) .GT. SQRT(BIG/N) * EPSLN / SFMIN, +* as a precaution measure, the full SVD is computed using CGESVJ +* with accumulated Jacobi rotations. This provides numerically +* more robust computation, at the cost of slightly increased run +* time. Depending on the concrete implementation of BLAS and LAPACK +* (i.e. how they behave in presence of extreme ill-conditioning) the +* implementor may decide to remove this switch. + IF ( ( AAQQ.LT.SQRT(SFMIN) ) .AND. LSVEC .AND. RSVEC ) THEN + JRACC = .TRUE. + END IF +* + END IF + IF ( AAQQ .LT. XSC ) THEN + DO 700 p = 1, N + IF ( SVA(p) .LT. XSC ) THEN + CALL CLASET( 'A', M, 1, CZERO, CZERO, A(1,p), LDA ) + SVA(p) = ZERO + END IF + 700 CONTINUE + END IF +* +* Preconditioning using QR factorization with pivoting +* + IF ( ROWPIV ) THEN +* Optional row permutation (Bjoerck row pivoting): +* A result by Cox and Higham shows that the Bjoerck's +* row pivoting combined with standard column pivoting +* has similar effect as Powell-Reid complete pivoting. +* The ell-infinity norms of A are made nonincreasing. + IF ( ( LSVEC .AND. RSVEC ) .AND. .NOT.( JRACC ) ) THEN + IWOFF = 2*N + ELSE + IWOFF = N + END IF + DO 1952 p = 1, M - 1 + q = ISAMAX( M-p+1, RWORK(M+p), 1 ) + p - 1 + IWORK(IWOFF+p) = q + IF ( p .NE. q ) THEN + TEMP1 = RWORK(M+p) + RWORK(M+p) = RWORK(M+q) + RWORK(M+q) = TEMP1 + END IF + 1952 CONTINUE + CALL CLASWP( N, A, LDA, 1, M-1, IWORK(IWOFF+1), 1 ) + END IF +* +* End of the preparation phase (scaling, optional sorting and +* transposing, optional flushing of small columns). +* +* Preconditioning +* +* If the full SVD is needed, the right singular vectors are computed +* from a matrix equation, and for that we need theoretical analysis +* of the Businger-Golub pivoting. So we use CGEQP3 as the first RR QRF. +* In all other cases the first RR QRF can be chosen by other criteria +* (eg speed by replacing global with restricted window pivoting, such +* as in xGEQPX from TOMS # 782). Good results will be obtained using +* xGEQPX with properly (!) chosen numerical parameters. +* Any improvement of CGEQP3 improves overall performance of CGEJSV. +* +* A * P1 = Q1 * [ R1^* 0]^*: + DO 1963 p = 1, N +* .. all columns are free columns + IWORK(p) = 0 + 1963 CONTINUE + CALL CGEQP3( M, N, A, LDA, IWORK, CWORK, CWORK(N+1), LWORK-N, + $ RWORK, IERR ) +* +* The upper triangular matrix R1 from the first QRF is inspected for +* rank deficiency and possibilities for deflation, or possible +* ill-conditioning. Depending on the user specified flag L2RANK, +* the procedure explores possibilities to reduce the numerical +* rank by inspecting the computed upper triangular factor. If +* L2RANK or L2ABER are up, then CGEJSV will compute the SVD of +* A + dA, where ||dA|| <= f(M,N)*EPSLN. +* + NR = 1 + IF ( L2ABER ) THEN +* Standard absolute error bound suffices. All sigma_i with +* sigma_i < N*EPSLN*||A|| are flushed to zero. This is an +* aggressive enforcement of lower numerical rank by introducing a +* backward error of the order of N*EPSLN*||A||. + TEMP1 = SQRT(REAL(N))*EPSLN + DO 3001 p = 2, N + IF ( ABS(A(p,p)) .GE. (TEMP1*ABS(A(1,1))) ) THEN + NR = NR + 1 + ELSE + GO TO 3002 + END IF + 3001 CONTINUE + 3002 CONTINUE + ELSE IF ( L2RANK ) THEN +* .. similarly as above, only slightly more gentle (less aggressive). +* Sudden drop on the diagonal of R1 is used as the criterion for +* close-to-rank-deficient. + TEMP1 = SQRT(SFMIN) + DO 3401 p = 2, N + IF ( ( ABS(A(p,p)) .LT. (EPSLN*ABS(A(p-1,p-1))) ) .OR. + $ ( ABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (ABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3402 + NR = NR + 1 + 3401 CONTINUE + 3402 CONTINUE +* + ELSE +* The goal is high relative accuracy. However, if the matrix +* has high scaled condition number the relative accuracy is in +* general not feasible. Later on, a condition number estimator +* will be deployed to estimate the scaled condition number. +* Here we just remove the underflowed part of the triangular +* factor. This prevents the situation in which the code is +* working hard to get the accuracy not warranted by the data. + TEMP1 = SQRT(SFMIN) + DO 3301 p = 2, N + IF ( ( ABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (ABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3302 + NR = NR + 1 + 3301 CONTINUE + 3302 CONTINUE +* + END IF +* + ALMORT = .FALSE. + IF ( NR .EQ. N ) THEN + MAXPRJ = ONE + DO 3051 p = 2, N + TEMP1 = ABS(A(p,p)) / SVA(IWORK(p)) + MAXPRJ = MIN( MAXPRJ, TEMP1 ) + 3051 CONTINUE + IF ( MAXPRJ**2 .GE. ONE - REAL(N)*EPSLN ) ALMORT = .TRUE. + END IF +* +* + SCONDA = - ONE + CONDR1 = - ONE + CONDR2 = - ONE +* + IF ( ERREST ) THEN + IF ( N .EQ. NR ) THEN + IF ( RSVEC ) THEN +* .. V is available as workspace + CALL CLACPY( 'U', N, N, A, LDA, V, LDV ) + DO 3053 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL CSSCAL( p, ONE/TEMP1, V(1,p), 1 ) + 3053 CONTINUE + IF ( LSVEC )THEN + CALL CPOCON( 'U', N, V, LDV, ONE, TEMP1, + $ CWORK(N+1), RWORK, IERR ) + ELSE + CALL CPOCON( 'U', N, V, LDV, ONE, TEMP1, + $ CWORK, RWORK, IERR ) + END IF +* + ELSE IF ( LSVEC ) THEN +* .. U is available as workspace + CALL CLACPY( 'U', N, N, A, LDA, U, LDU ) + DO 3054 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL CSSCAL( p, ONE/TEMP1, U(1,p), 1 ) + 3054 CONTINUE + CALL CPOCON( 'U', N, U, LDU, ONE, TEMP1, + $ CWORK(N+1), RWORK, IERR ) + ELSE + CALL CLACPY( 'U', N, N, A, LDA, CWORK, N ) +*[] CALL CLACPY( 'U', N, N, A, LDA, CWORK(N+1), N ) +* Change: here index shifted by N to the left, CWORK(1:N) +* not needed for SIGMA only computation + DO 3052 p = 1, N + TEMP1 = SVA(IWORK(p)) +*[] CALL CSSCAL( p, ONE/TEMP1, CWORK(N+(p-1)*N+1), 1 ) + CALL CSSCAL( p, ONE/TEMP1, CWORK((p-1)*N+1), 1 ) + 3052 CONTINUE +* .. the columns of R are scaled to have unit Euclidean lengths. +*[] CALL CPOCON( 'U', N, CWORK(N+1), N, ONE, TEMP1, +*[] $ CWORK(N+N*N+1), RWORK, IERR ) + CALL CPOCON( 'U', N, CWORK, N, ONE, TEMP1, + $ CWORK(N*N+1), RWORK, IERR ) +* + END IF + IF ( TEMP1 .NE. ZERO ) THEN + SCONDA = ONE / SQRT(TEMP1) + ELSE + SCONDA = - ONE + END IF +* SCONDA is an estimate of SQRT(||(R^* * R)^(-1)||_1). +* N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA + ELSE + SCONDA = - ONE + END IF + END IF +* + L2PERT = L2PERT .AND. ( ABS( A(1,1)/A(NR,NR) ) .GT. SQRT(BIG1) ) +* If there is no violent scaling, artificial perturbation is not needed. +* +* Phase 3: +* + IF ( .NOT. ( RSVEC .OR. LSVEC ) ) THEN +* +* Singular Values only +* +* .. transpose A(1:NR,1:N) + DO 1946 p = 1, MIN( N-1, NR ) + CALL CCOPY( N-p, A(p,p+1), LDA, A(p+1,p), 1 ) + CALL CLACGV( N-p+1, A(p,p), 1 ) + 1946 CONTINUE + IF ( NR .EQ. N ) A(N,N) = CONJG(A(N,N)) +* +* The following two DO-loops introduce small relative perturbation +* into the strict upper triangle of the lower triangular matrix. +* Small entries below the main diagonal are also changed. +* This modification is useful if the computing environment does not +* provide/allow FLUSH TO ZERO underflow, for it prevents many +* annoying denormalized numbers in case of strongly scaled matrices. +* The perturbation is structured so that it does not introduce any +* new perturbation of the singular values, and it does not destroy +* the job done by the preconditioner. +* The licence for this perturbation is in the variable L2PERT, which +* should be .FALSE. if FLUSH TO ZERO underflow is active. +* + IF ( .NOT. ALMORT ) THEN +* + IF ( L2PERT ) THEN +* XSC = SQRT(SMALL) + XSC = EPSLN / REAL(N) + DO 4947 q = 1, NR + CTEMP = CMPLX(XSC*ABS(A(q,q)),ZERO) + DO 4949 p = 1, N + IF ( ( (p.GT.q) .AND. (ABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) +* $ A(p,q) = TEMP1 * ( A(p,q) / ABS(A(p,q)) ) + $ A(p,q) = CTEMP + 4949 CONTINUE + 4947 CONTINUE + ELSE + CALL CLASET( 'U', NR-1,NR-1, CZERO,CZERO, A(1,2),LDA ) + END IF +* +* .. second preconditioning using the QR factorization +* + CALL CGEQRF( N,NR, A,LDA, CWORK, CWORK(N+1),LWORK-N, IERR ) +* +* .. and transpose upper to lower triangular + DO 1948 p = 1, NR - 1 + CALL CCOPY( NR-p, A(p,p+1), LDA, A(p+1,p), 1 ) + CALL CLACGV( NR-p+1, A(p,p), 1 ) + 1948 CONTINUE +* + END IF +* +* Row-cyclic Jacobi SVD algorithm with column pivoting +* +* .. again some perturbation (a "background noise") is added +* to drown denormals + IF ( L2PERT ) THEN +* XSC = SQRT(SMALL) + XSC = EPSLN / REAL(N) + DO 1947 q = 1, NR + CTEMP = CMPLX(XSC*ABS(A(q,q)),ZERO) + DO 1949 p = 1, NR + IF ( ( (p.GT.q) .AND. (ABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) +* $ A(p,q) = TEMP1 * ( A(p,q) / ABS(A(p,q)) ) + $ A(p,q) = CTEMP + 1949 CONTINUE + 1947 CONTINUE + ELSE + CALL CLASET( 'U', NR-1, NR-1, CZERO, CZERO, A(1,2), LDA ) + END IF +* +* .. and one-sided Jacobi rotations are started on a lower +* triangular matrix (plus perturbation which is ignored in +* the part which destroys triangular form (confusing?!)) +* + CALL CGESVJ( 'L', 'N', 'N', NR, NR, A, LDA, SVA, + $ N, V, LDV, CWORK, LWORK, RWORK, LRWORK, INFO ) +* + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) +* +* + ELSE IF ( ( RSVEC .AND. ( .NOT. LSVEC ) .AND. ( .NOT. JRACC ) ) + $ .OR. + $ ( JRACC .AND. ( .NOT. LSVEC ) .AND. ( NR .NE. N ) ) ) THEN +* +* -> Singular Values and Right Singular Vectors <- +* + IF ( ALMORT ) THEN +* +* .. in this case NR equals N + DO 1998 p = 1, NR + CALL CCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL CLACGV( N-p+1, V(p,p), 1 ) + 1998 CONTINUE + CALL CLASET( 'U', NR-1,NR-1, CZERO, CZERO, V(1,2), LDV ) +* + CALL CGESVJ( 'L','U','N', N, NR, V, LDV, SVA, NR, A, LDA, + $ CWORK, LWORK, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + + ELSE +* +* .. two more QR factorizations ( one QRF is not enough, two require +* accumulated product of Jacobi rotations, three are perfect ) +* + CALL CLASET( 'L', NR-1,NR-1, CZERO, CZERO, A(2,1), LDA ) + CALL CGELQF( NR,N, A, LDA, CWORK, CWORK(N+1), LWORK-N, IERR) + CALL CLACPY( 'L', NR, NR, A, LDA, V, LDV ) + CALL CLASET( 'U', NR-1,NR-1, CZERO, CZERO, V(1,2), LDV ) + CALL CGEQRF( NR, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) + DO 8998 p = 1, NR + CALL CCOPY( NR-p+1, V(p,p), LDV, V(p,p), 1 ) + CALL CLACGV( NR-p+1, V(p,p), 1 ) + 8998 CONTINUE + CALL CLASET('U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV) +* + CALL CGESVJ( 'L', 'U','N', NR, NR, V,LDV, SVA, NR, U, + $ LDU, CWORK(N+1), LWORK-N, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + IF ( NR .LT. N ) THEN + CALL CLASET( 'A',N-NR, NR, CZERO,CZERO, V(NR+1,1), LDV ) + CALL CLASET( 'A',NR, N-NR, CZERO,CZERO, V(1,NR+1), LDV ) + CALL CLASET( 'A',N-NR,N-NR,CZERO,CONE, V(NR+1,NR+1),LDV ) + END IF +* + CALL CUNMLQ( 'L', 'C', N, N, NR, A, LDA, CWORK, + $ V, LDV, CWORK(N+1), LWORK-N, IERR ) +* + END IF +* .. permute the rows of V +* DO 8991 p = 1, N +* CALL CCOPY( N, V(p,1), LDV, A(IWORK(p),1), LDA ) +* 8991 CONTINUE +* CALL CLACPY( 'All', N, N, A, LDA, V, LDV ) + CALL CLAPMR( .FALSE., N, N, V, LDV, IWORK ) +* + IF ( TRANSP ) THEN + CALL CLACPY( 'A', N, N, V, LDV, U, LDU ) + END IF +* + ELSE IF ( JRACC .AND. (.NOT. LSVEC) .AND. ( NR.EQ. N ) ) THEN +* + CALL CLASET( 'L', N-1,N-1, CZERO, CZERO, A(2,1), LDA ) +* + CALL CGESVJ( 'U','N','V', N, N, A, LDA, SVA, N, V, LDV, + $ CWORK, LWORK, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + CALL CLAPMR( .FALSE., N, N, V, LDV, IWORK ) +* + ELSE IF ( LSVEC .AND. ( .NOT. RSVEC ) ) THEN +* +* .. Singular Values and Left Singular Vectors .. +* +* .. second preconditioning step to avoid need to accumulate +* Jacobi rotations in the Jacobi iterations. + DO 1965 p = 1, NR + CALL CCOPY( N-p+1, A(p,p), LDA, U(p,p), 1 ) + CALL CLACGV( N-p+1, U(p,p), 1 ) + 1965 CONTINUE + CALL CLASET( 'U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) +* + CALL CGEQRF( N, NR, U, LDU, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) +* + DO 1967 p = 1, NR - 1 + CALL CCOPY( NR-p, U(p,p+1), LDU, U(p+1,p), 1 ) + CALL CLACGV( N-p+1, U(p,p), 1 ) + 1967 CONTINUE + CALL CLASET( 'U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) +* + CALL CGESVJ( 'L', 'U', 'N', NR,NR, U, LDU, SVA, NR, A, + $ LDA, CWORK(N+1), LWORK-N, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) +* + IF ( NR .LT. M ) THEN + CALL CLASET( 'A', M-NR, NR,CZERO, CZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL CLASET( 'A',NR, N1-NR, CZERO, CZERO, U(1,NR+1),LDU ) + CALL CLASET( 'A',M-NR,N1-NR,CZERO,CONE,U(NR+1,NR+1),LDU ) + END IF + END IF +* + CALL CUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL CLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + DO 1974 p = 1, N1 + XSC = ONE / SCNRM2( M, U(1,p), 1 ) + CALL CSSCAL( M, XSC, U(1,p), 1 ) + 1974 CONTINUE +* + IF ( TRANSP ) THEN + CALL CLACPY( 'A', N, N, U, LDU, V, LDV ) + END IF +* + ELSE +* +* .. Full SVD .. +* + IF ( .NOT. JRACC ) THEN +* + IF ( .NOT. ALMORT ) THEN +* +* Second Preconditioning Step (QRF [with pivoting]) +* Note that the composition of TRANSPOSE, QRF and TRANSPOSE is +* equivalent to an LQF CALL. Since in many libraries the QRF +* seems to be better optimized than the LQF, we do explicit +* transpose and use the QRF. This is subject to changes in an +* optimized implementation of CGEJSV. +* + DO 1968 p = 1, NR + CALL CCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL CLACGV( N-p+1, V(p,p), 1 ) + 1968 CONTINUE +* +* .. the following two loops perturb small entries to avoid +* denormals in the second QR factorization, where they are +* as good as zeros. This is done to avoid painfully slow +* computation with denormals. The relative size of the perturbation +* is a parameter that can be changed by the implementer. +* This perturbation device will be obsolete on machines with +* properly implemented arithmetic. +* To switch it off, set L2PERT=.FALSE. To remove it from the +* code, remove the action under L2PERT=.TRUE., leave the ELSE part. +* The following two loops should be blocked and fused with the +* transposed copy above. +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 2969 q = 1, NR + CTEMP = CMPLX(XSC*ABS( V(q,q) ),ZERO) + DO 2968 p = 1, N + IF ( ( p .GT. q ) .AND. ( ABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) +* $ V(p,q) = TEMP1 * ( V(p,q) / ABS(V(p,q)) ) + $ V(p,q) = CTEMP + IF ( p .LT. q ) V(p,q) = - V(p,q) + 2968 CONTINUE + 2969 CONTINUE + ELSE + CALL CLASET( 'U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV ) + END IF +* +* Estimate the row scaled condition number of R1 +* (If R1 is rectangular, N > NR, then the condition number +* of the leading NR x NR submatrix is estimated.) +* + CALL CLACPY( 'L', NR, NR, V, LDV, CWORK(2*N+1), NR ) + DO 3950 p = 1, NR + TEMP1 = SCNRM2(NR-p+1,CWORK(2*N+(p-1)*NR+p),1) + CALL CSSCAL(NR-p+1,ONE/TEMP1,CWORK(2*N+(p-1)*NR+p),1) + 3950 CONTINUE + CALL CPOCON('L',NR,CWORK(2*N+1),NR,ONE,TEMP1, + $ CWORK(2*N+NR*NR+1),RWORK,IERR) + CONDR1 = ONE / SQRT(TEMP1) +* .. here need a second opinion on the condition number +* .. then assume worst case scenario +* R1 is OK for inverse <=> CONDR1 .LT. REAL(N) +* more conservative <=> CONDR1 .LT. SQRT(REAL(N)) +* + COND_OK = SQRT(SQRT(REAL(NR))) +*[TP] COND_OK is a tuning parameter. +* + IF ( CONDR1 .LT. COND_OK ) THEN +* .. the second QRF without pivoting. Note: in an optimized +* implementation, this QRF should be implemented as the QRF +* of a lower triangular matrix. +* R1^* = Q2 * R2 + CALL CGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL)/EPSLN + DO 3959 p = 2, NR + DO 3958 q = 1, p - 1 + CTEMP=CMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) + IF ( ABS(V(q,p)) .LE. TEMP1 ) +* $ V(q,p) = TEMP1 * ( V(q,p) / ABS(V(q,p)) ) + $ V(q,p) = CTEMP + 3958 CONTINUE + 3959 CONTINUE + END IF +* + IF ( NR .NE. N ) + $ CALL CLACPY( 'A', N, NR, V, LDV, CWORK(2*N+1), N ) +* .. save ... +* +* .. this transposed copy should be better than naive + DO 1969 p = 1, NR - 1 + CALL CCOPY( NR-p, V(p,p+1), LDV, V(p+1,p), 1 ) + CALL CLACGV(NR-p+1, V(p,p), 1 ) + 1969 CONTINUE + V(NR,NR)=CONJG(V(NR,NR)) +* + CONDR2 = CONDR1 +* + ELSE +* +* .. ill-conditioned case: second QRF with pivoting +* Note that windowed pivoting would be equally good +* numerically, and more run-time efficient. So, in +* an optimal implementation, the next call to CGEQP3 +* should be replaced with eg. CALL CGEQPX (ACM TOMS #782) +* with properly (carefully) chosen parameters. +* +* R1^* * P2 = Q2 * R2 + DO 3003 p = 1, NR + IWORK(N+p) = 0 + 3003 CONTINUE + CALL CGEQP3( N, NR, V, LDV, IWORK(N+1), CWORK(N+1), + $ CWORK(2*N+1), LWORK-2*N, RWORK, IERR ) +** CALL CGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), +** $ LWORK-2*N, IERR ) + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 3969 p = 2, NR + DO 3968 q = 1, p - 1 + CTEMP=CMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) + IF ( ABS(V(q,p)) .LE. TEMP1 ) +* $ V(q,p) = TEMP1 * ( V(q,p) / ABS(V(q,p)) ) + $ V(q,p) = CTEMP + 3968 CONTINUE + 3969 CONTINUE + END IF +* + CALL CLACPY( 'A', N, NR, V, LDV, CWORK(2*N+1), N ) +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 8970 p = 2, NR + DO 8971 q = 1, p - 1 + CTEMP=CMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) +* V(p,q) = - TEMP1*( V(q,p) / ABS(V(q,p)) ) + V(p,q) = - CTEMP + 8971 CONTINUE + 8970 CONTINUE + ELSE + CALL CLASET( 'L',NR-1,NR-1,CZERO,CZERO,V(2,1),LDV ) + END IF +* Now, compute R2 = L3 * Q3, the LQ factorization. + CALL CGELQF( NR, NR, V, LDV, CWORK(2*N+N*NR+1), + $ CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, IERR ) +* .. and estimate the condition number + CALL CLACPY( 'L',NR,NR,V,LDV,CWORK(2*N+N*NR+NR+1),NR ) + DO 4950 p = 1, NR + TEMP1 = SCNRM2( p, CWORK(2*N+N*NR+NR+p), NR ) + CALL CSSCAL( p, ONE/TEMP1, CWORK(2*N+N*NR+NR+p), NR ) + 4950 CONTINUE + CALL CPOCON( 'L',NR,CWORK(2*N+N*NR+NR+1),NR,ONE,TEMP1, + $ CWORK(2*N+N*NR+NR+NR*NR+1),RWORK,IERR ) + CONDR2 = ONE / SQRT(TEMP1) +* +* + IF ( CONDR2 .GE. COND_OK ) THEN +* .. save the Householder vectors used for Q3 +* (this overwrites the copy of R2, as it will not be +* needed in this branch, but it does not overwritte the +* Huseholder vectors of Q2.). + CALL CLACPY( 'U', NR, NR, V, LDV, CWORK(2*N+1), N ) +* .. and the rest of the information on Q3 is in +* WORK(2*N+N*NR+1:2*N+N*NR+N) + END IF +* + END IF +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 4968 q = 2, NR + CTEMP = XSC * V(q,q) + DO 4969 p = 1, q - 1 +* V(p,q) = - TEMP1*( V(p,q) / ABS(V(p,q)) ) + V(p,q) = - CTEMP + 4969 CONTINUE + 4968 CONTINUE + ELSE + CALL CLASET( 'U', NR-1,NR-1, CZERO,CZERO, V(1,2), LDV ) + END IF +* +* Second preconditioning finished; continue with Jacobi SVD +* The input matrix is lower trinagular. +* +* Recover the right singular vectors as solution of a well +* conditioned triangular matrix equation. +* + IF ( CONDR1 .LT. COND_OK ) THEN +* + CALL CGESVJ( 'L','U','N',NR,NR,V,LDV,SVA,NR,U, LDU, + $ CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,RWORK, + $ LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 3970 p = 1, NR + CALL CCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL CSSCAL( NR, SVA(p), V(1,p), 1 ) + 3970 CONTINUE + +* .. pick the right matrix equation and solve it +* + IF ( NR .EQ. N ) THEN +* :)) .. best case, R1 is inverted. The solution of this matrix +* equation is Q2*V2 = the product of the Jacobi rotations +* used in CGESVJ, premultiplied with the orthogonal matrix +* from the second QR factorization. + CALL CTRSM('L','U','N','N', NR,NR,CONE, A,LDA, V,LDV) + ELSE +* .. R1 is well conditioned, but non-square. Adjoint of R2 +* is inverted to get the product of the Jacobi rotations +* used in CGESVJ. The Q-factor from the second QR +* factorization is then built in explicitly. + CALL CTRSM('L','U','C','N',NR,NR,CONE,CWORK(2*N+1), + $ N,V,LDV) + IF ( NR .LT. N ) THEN + CALL CLASET('A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV) + CALL CLASET('A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV) + CALL CLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL CUNMQR('L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR) + END IF +* + ELSE IF ( CONDR2 .LT. COND_OK ) THEN +* +* The matrix R2 is inverted. The solution of the matrix equation +* is Q3^* * V3 = the product of the Jacobi rotations (appplied to +* the lower triangular L3 from the LQ factorization of +* R2=L3*Q3), pre-multiplied with the transposed Q3. + CALL CGESVJ( 'L', 'U', 'N', NR, NR, V, LDV, SVA, NR, U, + $ LDU, CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 3870 p = 1, NR + CALL CCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL CSSCAL( NR, SVA(p), U(1,p), 1 ) + 3870 CONTINUE + CALL CTRSM('L','U','N','N',NR,NR,CONE,CWORK(2*N+1),N, + $ U,LDU) +* .. apply the permutation from the second QR factorization + DO 873 q = 1, NR + DO 872 p = 1, NR + CWORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 872 CONTINUE + DO 874 p = 1, NR + U(p,q) = CWORK(2*N+N*NR+NR+p) + 874 CONTINUE + 873 CONTINUE + IF ( NR .LT. N ) THEN + CALL CLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL CLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL CLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL CUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) + ELSE +* Last line of defense. +* #:( This is a rather pathological case: no scaled condition +* improvement after two pivoted QR factorizations. Other +* possibility is that the rank revealing QR factorization +* or the condition estimator has failed, or the COND_OK +* is set very close to ONE (which is unnecessary). Normally, +* this branch should never be executed, but in rare cases of +* failure of the RRQR or condition estimator, the last line of +* defense ensures that CGEJSV completes the task. +* Compute the full SVD of L3 using CGESVJ with explicit +* accumulation of Jacobi rotations. + CALL CGESVJ( 'L', 'U', 'V', NR, NR, V, LDV, SVA, NR, U, + $ LDU, CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + IF ( NR .LT. N ) THEN + CALL CLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL CLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL CLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL CUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* + CALL CUNMLQ( 'L', 'C', NR, NR, NR, CWORK(2*N+1), N, + $ CWORK(2*N+N*NR+1), U, LDU, CWORK(2*N+N*NR+NR+1), + $ LWORK-2*N-N*NR-NR, IERR ) + DO 773 q = 1, NR + DO 772 p = 1, NR + CWORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 772 CONTINUE + DO 774 p = 1, NR + U(p,q) = CWORK(2*N+N*NR+NR+p) + 774 CONTINUE + 773 CONTINUE +* + END IF +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = SQRT(REAL(N)) * EPSLN + DO 1972 q = 1, N + DO 972 p = 1, N + CWORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 972 CONTINUE + DO 973 p = 1, N + V(p,q) = CWORK(2*N+N*NR+NR+p) + 973 CONTINUE + XSC = ONE / SCNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL CSSCAL( N, XSC, V(1,q), 1 ) + 1972 CONTINUE +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). + IF ( NR .LT. M ) THEN + CALL CLASET('A', M-NR, NR, CZERO, CZERO, U(NR+1,1), LDU) + IF ( NR .LT. N1 ) THEN + CALL CLASET('A',NR,N1-NR,CZERO,CZERO,U(1,NR+1),LDU) + CALL CLASET('A',M-NR,N1-NR,CZERO,CONE, + $ U(NR+1,NR+1),LDU) + END IF + END IF +* +* The Q matrix from the first QRF is built into the left singular +* matrix U. This applies to all cases. +* + CALL CUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) + +* The columns of U are normalized. The cost is O(M*N) flops. + TEMP1 = SQRT(REAL(M)) * EPSLN + DO 1973 p = 1, NR + XSC = ONE / SCNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL CSSCAL( M, XSC, U(1,p), 1 ) + 1973 CONTINUE +* +* If the initial QRF is computed with row pivoting, the left +* singular vectors must be adjusted. +* + IF ( ROWPIV ) + $ CALL CLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + ELSE +* +* .. the initial matrix A has almost orthogonal columns and +* the second QRF is not needed +* + CALL CLACPY( 'U', N, N, A, LDA, CWORK(N+1), N ) + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 5970 p = 2, N + CTEMP = XSC * CWORK( N + (p-1)*N + p ) + DO 5971 q = 1, p - 1 +* CWORK(N+(q-1)*N+p)=-TEMP1 * ( CWORK(N+(p-1)*N+q) / +* $ ABS(CWORK(N+(p-1)*N+q)) ) + CWORK(N+(q-1)*N+p)=-CTEMP + 5971 CONTINUE + 5970 CONTINUE + ELSE + CALL CLASET( 'L',N-1,N-1,CZERO,CZERO,CWORK(N+2),N ) + END IF +* + CALL CGESVJ( 'U', 'U', 'N', N, N, CWORK(N+1), N, SVA, + $ N, U, LDU, CWORK(N+N*N+1), LWORK-N-N*N, RWORK, LRWORK, + $ INFO ) +* + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 6970 p = 1, N + CALL CCOPY( N, CWORK(N+(p-1)*N+1), 1, U(1,p), 1 ) + CALL CSSCAL( N, SVA(p), CWORK(N+(p-1)*N+1), 1 ) + 6970 CONTINUE +* + CALL CTRSM( 'L', 'U', 'N', 'N', N, N, + $ CONE, A, LDA, CWORK(N+1), N ) + DO 6972 p = 1, N + CALL CCOPY( N, CWORK(N+p), N, V(IWORK(p),1), LDV ) + 6972 CONTINUE + TEMP1 = SQRT(REAL(N))*EPSLN + DO 6971 p = 1, N + XSC = ONE / SCNRM2( N, V(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL CSSCAL( N, XSC, V(1,p), 1 ) + 6971 CONTINUE +* +* Assemble the left singular vector matrix U (M x N). +* + IF ( N .LT. M ) THEN + CALL CLASET( 'A', M-N, N, CZERO, CZERO, U(N+1,1), LDU ) + IF ( N .LT. N1 ) THEN + CALL CLASET('A',N, N1-N, CZERO, CZERO, U(1,N+1),LDU) + CALL CLASET( 'A',M-N,N1-N, CZERO, CONE,U(N+1,N+1),LDU) + END IF + END IF + CALL CUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) + TEMP1 = SQRT(REAL(M))*EPSLN + DO 6973 p = 1, N1 + XSC = ONE / SCNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL CSSCAL( M, XSC, U(1,p), 1 ) + 6973 CONTINUE +* + IF ( ROWPIV ) + $ CALL CLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + END IF +* +* end of the >> almost orthogonal case << in the full SVD +* + ELSE +* +* This branch deploys a preconditioned Jacobi SVD with explicitly +* accumulated rotations. It is included as optional, mainly for +* experimental purposes. It does perform well, and can also be used. +* In this implementation, this branch will be automatically activated +* if the condition number sigma_max(A) / sigma_min(A) is predicted +* to be greater than the overflow threshold. This is because the +* a posteriori computation of the singular vectors assumes robust +* implementation of BLAS and some LAPACK procedures, capable of working +* in presence of extreme values, e.g. when the singular values spread from +* the underflow to the overflow threshold. +* + DO 7968 p = 1, NR + CALL CCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL CLACGV( N-p+1, V(p,p), 1 ) + 7968 CONTINUE +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL/EPSLN) + DO 5969 q = 1, NR + CTEMP = CMPLX(XSC*ABS( V(q,q) ),ZERO) + DO 5968 p = 1, N + IF ( ( p .GT. q ) .AND. ( ABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) +* $ V(p,q) = TEMP1 * ( V(p,q) / ABS(V(p,q)) ) + $ V(p,q) = CTEMP + IF ( p .LT. q ) V(p,q) = - V(p,q) + 5968 CONTINUE + 5969 CONTINUE + ELSE + CALL CLASET( 'U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV ) + END IF + + CALL CGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) + CALL CLACPY( 'L', N, NR, V, LDV, CWORK(2*N+1), N ) +* + DO 7969 p = 1, NR + CALL CCOPY( NR-p+1, V(p,p), LDV, U(p,p), 1 ) + CALL CLACGV( NR-p+1, U(p,p), 1 ) + 7969 CONTINUE + + IF ( L2PERT ) THEN + XSC = SQRT(SMALL/EPSLN) + DO 9970 q = 2, NR + DO 9971 p = 1, q - 1 + CTEMP = CMPLX(XSC * MIN(ABS(U(p,p)),ABS(U(q,q))), + $ ZERO) +* U(p,q) = - TEMP1 * ( U(q,p) / ABS(U(q,p)) ) + U(p,q) = - CTEMP + 9971 CONTINUE + 9970 CONTINUE + ELSE + CALL CLASET('U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) + END IF + + CALL CGESVJ( 'L', 'U', 'V', NR, NR, U, LDU, SVA, + $ N, V, LDV, CWORK(2*N+N*NR+1), LWORK-2*N-N*NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + + IF ( NR .LT. N ) THEN + CALL CLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL CLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL CLASET( 'A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV ) + END IF + + CALL CUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = SQRT(REAL(N)) * EPSLN + DO 7972 q = 1, N + DO 8972 p = 1, N + CWORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 8972 CONTINUE + DO 8973 p = 1, N + V(p,q) = CWORK(2*N+N*NR+NR+p) + 8973 CONTINUE + XSC = ONE / SCNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL CSSCAL( N, XSC, V(1,q), 1 ) + 7972 CONTINUE +* +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). +* + IF ( NR .LT. M ) THEN + CALL CLASET( 'A', M-NR, NR, CZERO, CZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL CLASET('A',NR, N1-NR, CZERO, CZERO, U(1,NR+1),LDU) + CALL CLASET('A',M-NR,N1-NR, CZERO, CONE,U(NR+1,NR+1),LDU) + END IF + END IF +* + CALL CUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL CLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* +* + END IF + IF ( TRANSP ) THEN +* .. swap U and V because the procedure worked on A^* + DO 6974 p = 1, N + CALL CSWAP( N, U(1,p), 1, V(1,p), 1 ) + 6974 CONTINUE + END IF +* + END IF +* end of the full SVD +* +* Undo scaling, if necessary (and possible) +* + IF ( USCAL2 .LE. (BIG/SVA(1))*USCAL1 ) THEN + CALL SLASCL( 'G', 0, 0, USCAL1, USCAL2, NR, 1, SVA, N, IERR ) + USCAL1 = ONE + USCAL2 = ONE + END IF +* + IF ( NR .LT. N ) THEN + DO 3004 p = NR+1, N + SVA(p) = ZERO + 3004 CONTINUE + END IF +* + RWORK(1) = USCAL2 * SCALEM + RWORK(2) = USCAL1 + IF ( ERREST ) RWORK(3) = SCONDA + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = CONDR1 + RWORK(5) = CONDR2 + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ENTRA + RWORK(7) = ENTRAT + END IF +* + IWORK(1) = NR + IWORK(2) = NUMRANK + IWORK(3) = WARNING + IF ( TRANSP ) THEN + IWORK(4) = 1 + ELSE + IWORK(4) = -1 + END IF + +* + RETURN +* .. +* .. END OF CGEJSV +* .. + END +* diff --git a/lapack-netlib/cgesvx.f b/lapack-netlib/cgesvx.f new file mode 100644 index 000000000..74a37e9a0 --- /dev/null +++ b/lapack-netlib/cgesvx.f @@ -0,0 +1,602 @@ +*> \brief CGESVX computes the solution to system of linear equations A * X = B for GE matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download CGESVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE CGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, +* EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, +* WORK, RWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS +* REAL RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ) +* REAL BERR( * ), C( * ), FERR( * ), R( * ), +* $ RWORK( * ) +* COMPLEX A( LDA, * ), AF( LDAF, * ), B( LDB, * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> CGESVX uses the LU factorization to compute the solution to a complex +*> system of linear equations +*> A * X = B, +*> where A is an N-by-N matrix and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = P * L * U, +*> where P is a permutation matrix, L is a unit lower triangular +*> matrix, and U is upper triangular. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AF and IPIV contain the factored form of A. +*> If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> A, AF, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AF and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AF and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations: +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Conjugate transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is COMPLEX array, dimension (LDA,N) +*> On entry, the N-by-N matrix A. If FACT = 'F' and EQUED is +*> not 'N', then A must have been equilibrated by the scaling +*> factors in R and/or C. A is not modified if FACT = 'F' or +*> 'N', or if FACT = 'E' and EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] AF +*> \verbatim +*> AF is COMPLEX array, dimension (LDAF,N) +*> If FACT = 'F', then AF is an input argument and on entry +*> contains the factors L and U from the factorization +*> A = P*L*U as computed by CGETRF. If EQUED .ne. 'N', then +*> AF is the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the equilibrated matrix A (see the description of A for +*> the form of the equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAF +*> \verbatim +*> LDAF is INTEGER +*> The leading dimension of the array AF. LDAF >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = P*L*U +*> as computed by CGETRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is REAL array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is REAL array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is COMPLEX array, dimension (LDB,NRHS) +*> On entry, the N-by-NRHS right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is COMPLEX array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is REAL +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is REAL array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is REAL array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is COMPLEX array, dimension (2*N) +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is REAL array, dimension (MAX(1,2*N)) +*> On exit, RWORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If RWORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 RWORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization has +*> been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complexGEsolve +* +* ===================================================================== + SUBROUTINE CGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, + $ EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, + $ WORK, RWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS + REAL RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ) + REAL BERR( * ), C( * ), FERR( * ), R( * ), + $ RWORK( * ) + COMPLEX A( LDA, * ), AF( LDAF, * ), B( LDB, * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* +* .. Parameters .. + REAL ZERO, ONE + PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J + REAL AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + REAL CLANGE, CLANTR, SLAMCH + EXTERNAL LSAME, CLANGE, CLANTR, SLAMCH +* .. +* .. External Subroutines .. + EXTERNAL CGECON, CGEEQU, CGERFS, CGETRF, CGETRS, CLACPY, + $ CLAQGE, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = SLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -4 + ELSE IF( LDA.LT.MAX( 1, N ) ) THEN + INFO = -6 + ELSE IF( LDAF.LT.MAX( 1, N ) ) THEN + INFO = -8 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -10 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -11 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -12 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -14 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -16 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'CGESVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL CGEEQU( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL CLAQGE( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, + $ EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of A. +* + CALL CLACPY( 'Full', N, N, A, LDA, AF, LDAF ) + CALL CGETRF( N, N, AF, LDAF, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + RPVGRW = CLANTR( 'M', 'U', 'N', INFO, INFO, AF, LDAF, + $ RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = CLANGE( 'M', N, INFO, A, LDA, RWORK ) / + $ RPVGRW + END IF + RWORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = CLANGE( NORM, N, N, A, LDA, RWORK ) + RPVGRW = CLANTR( 'M', 'U', 'N', N, N, AF, LDAF, RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = CLANGE( 'M', N, N, A, LDA, RWORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL CGECON( NORM, N, AF, LDAF, ANORM, RCOND, WORK, RWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL CLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL CGETRS( TRANS, N, NRHS, AF, LDAF, IPIV, X, LDX, INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL CGERFS( TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, B, LDB, X, + $ LDX, FERR, BERR, WORK, RWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 80 J = 1, NRHS + DO 70 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 70 CONTINUE + 80 CONTINUE + DO 90 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 90 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 120 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.SLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + RWORK( 1 ) = RPVGRW + RETURN +* +* End of CGESVX +* + END diff --git a/lapack-netlib/dgbsvx.f b/lapack-netlib/dgbsvx.f new file mode 100644 index 000000000..0ee5eecb3 --- /dev/null +++ b/lapack-netlib/dgbsvx.f @@ -0,0 +1,639 @@ +*> \brief DGBSVX computes the solution to system of linear equations A * X = B for GB matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download DGBSVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE DGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, +* LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, +* RCOND, FERR, BERR, WORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS +* DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ), IWORK( * ) +* DOUBLE PRECISION AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), +* $ BERR( * ), C( * ), FERR( * ), R( * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> DGBSVX uses the LU factorization to compute the solution to a real +*> system of linear equations A * X = B, A**T * X = B, or A**H * X = B, +*> where A is a band matrix of order N with KL subdiagonals and KU +*> superdiagonals, and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed by this subroutine: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = L * U, +*> where L is a product of permutation and unit lower triangular +*> matrices with KL subdiagonals, and U is upper triangular with +*> KL+KU superdiagonals. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AFB and IPIV contain the factored form of +*> A. If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> AB, AFB, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AFB and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AFB and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations. +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] KL +*> \verbatim +*> KL is INTEGER +*> The number of subdiagonals within the band of A. KL >= 0. +*> \endverbatim +*> +*> \param[in] KU +*> \verbatim +*> KU is INTEGER +*> The number of superdiagonals within the band of A. KU >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] AB +*> \verbatim +*> AB is DOUBLE PRECISION array, dimension (LDAB,N) +*> On entry, the matrix A in band storage, in rows 1 to KL+KU+1. +*> The j-th column of A is stored in the j-th column of the +*> array AB as follows: +*> AB(KU+1+i-j,j) = A(i,j) for max(1,j-KU)<=i<=min(N,j+kl) +*> +*> If FACT = 'F' and EQUED is not 'N', then A must have been +*> equilibrated by the scaling factors in R and/or C. AB is not +*> modified if FACT = 'F' or 'N', or if FACT = 'E' and +*> EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDAB +*> \verbatim +*> LDAB is INTEGER +*> The leading dimension of the array AB. LDAB >= KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] AFB +*> \verbatim +*> AFB is DOUBLE PRECISION array, dimension (LDAFB,N) +*> If FACT = 'F', then AFB is an input argument and on entry +*> contains details of the LU factorization of the band matrix +*> A, as computed by DGBTRF. U is stored as an upper triangular +*> band matrix with KL+KU superdiagonals in rows 1 to KL+KU+1, +*> and the multipliers used during the factorization are stored +*> in rows KL+KU+2 to 2*KL+KU+1. If EQUED .ne. 'N', then AFB is +*> the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AFB is an output argument and on exit +*> returns details of the LU factorization of A. +*> +*> If FACT = 'E', then AFB is an output argument and on exit +*> returns details of the LU factorization of the equilibrated +*> matrix A (see the description of AB for the form of the +*> equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAFB +*> \verbatim +*> LDAFB is INTEGER +*> The leading dimension of the array AFB. LDAFB >= 2*KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = L*U +*> as computed by DGBTRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is DOUBLE PRECISION array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is DOUBLE PRECISION array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is DOUBLE PRECISION array, dimension (LDB,NRHS) +*> On entry, the right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is DOUBLE PRECISION array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is DOUBLE PRECISION +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is DOUBLE PRECISION array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is DOUBLE PRECISION array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is DOUBLE PRECISION array, dimension (MAX(1,3*N)) +*> On exit, WORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If WORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 WORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, dimension (N) +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization +*> has been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup doubleGBsolve +* +* ===================================================================== + SUBROUTINE DGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, + $ LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, + $ RCOND, FERR, BERR, WORK, IWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS + DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ), IWORK( * ) + DOUBLE PRECISION AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), + $ BERR( * ), C( * ), FERR( * ), R( * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* +* .. Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J, J1, J2 + DOUBLE PRECISION AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + DOUBLE PRECISION DLAMCH, DLANGB, DLANTB + EXTERNAL LSAME, DLAMCH, DLANGB, DLANTB +* .. +* .. External Subroutines .. + EXTERNAL DCOPY, DGBCON, DGBEQU, DGBRFS, DGBTRF, DGBTRS, + $ DLACPY, DLAQGB, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC ABS, MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = DLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( KL.LT.0 ) THEN + INFO = -4 + ELSE IF( KU.LT.0 ) THEN + INFO = -5 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -6 + ELSE IF( LDAB.LT.KL+KU+1 ) THEN + INFO = -8 + ELSE IF( LDAFB.LT.2*KL+KU+1 ) THEN + INFO = -10 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -12 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -13 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -14 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -16 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -18 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'DGBSVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL DGBEQU( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL DLAQGB( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of the band matrix A. +* + DO 70 J = 1, N + J1 = MAX( J-KU, 1 ) + J2 = MIN( J+KL, N ) + CALL DCOPY( J2-J1+1, AB( KU+1-J+J1, J ), 1, + $ AFB( KL+KU+1-J+J1, J ), 1 ) + 70 CONTINUE +* + CALL DGBTRF( N, N, KL, KU, AFB, LDAFB, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + ANORM = ZERO + DO 90 J = 1, INFO + DO 80 I = MAX( KU+2-J, 1 ), MIN( N+KU+1-J, KL+KU+1 ) + ANORM = MAX( ANORM, ABS( AB( I, J ) ) ) + 80 CONTINUE + 90 CONTINUE + RPVGRW = DLANTB( 'M', 'U', 'N', INFO, MIN( INFO-1, KL+KU ), + $ AFB( MAX( 1, KL+KU+2-INFO ), 1 ), LDAFB, + $ WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ANORM / RPVGRW + END IF + WORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = DLANGB( NORM, N, KL, KU, AB, LDAB, WORK ) + RPVGRW = DLANTB( 'M', 'U', 'N', N, KL+KU, AFB, LDAFB, WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = DLANGB( 'M', N, KL, KU, AB, LDAB, WORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL DGBCON( NORM, N, KL, KU, AFB, LDAFB, IPIV, ANORM, RCOND, + $ WORK, IWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL DLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL DGBTRS( TRANS, N, KL, KU, NRHS, AFB, LDAFB, IPIV, X, LDX, + $ INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL DGBRFS( TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, LDAFB, IPIV, + $ B, LDB, X, LDX, FERR, BERR, WORK, IWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 120 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 140 J = 1, NRHS + DO 130 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 130 CONTINUE + 140 CONTINUE + DO 150 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 150 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.DLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + WORK( 1 ) = RPVGRW + RETURN +* +* End of DGBSVX +* + END diff --git a/lapack-netlib/dgejsv.f b/lapack-netlib/dgejsv.f new file mode 100644 index 000000000..ee769bb38 --- /dev/null +++ b/lapack-netlib/dgejsv.f @@ -0,0 +1,1780 @@ +*> \brief \b DGEJSV +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download DGEJSV + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE DGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, +* M, N, A, LDA, SVA, U, LDU, V, LDV, +* WORK, LWORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* IMPLICIT NONE +* INTEGER INFO, LDA, LDU, LDV, LWORK, M, N +* .. +* .. Array Arguments .. +* DOUBLE PRECISION A( LDA, * ), SVA( N ), U( LDU, * ), V( LDV, * ), +* $ WORK( LWORK ) +* INTEGER IWORK( * ) +* CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> DGEJSV computes the singular value decomposition (SVD) of a real M-by-N +*> matrix [A], where M >= N. The SVD of [A] is written as +*> +*> [A] = [U] * [SIGMA] * [V]^t, +*> +*> where [SIGMA] is an N-by-N (M-by-N) matrix which is zero except for its N +*> diagonal elements, [U] is an M-by-N (or M-by-M) orthonormal matrix, and +*> [V] is an N-by-N orthogonal matrix. The diagonal elements of [SIGMA] are +*> the singular values of [A]. The columns of [U] and [V] are the left and +*> the right singular vectors of [A], respectively. The matrices [U] and [V] +*> are computed and stored in the arrays U and V, respectively. The diagonal +*> of [SIGMA] is computed and stored in the array SVA. +*> DGEJSV can sometimes compute tiny singular values and their singular vectors much +*> more accurately than other SVD routines, see below under Further Details. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] JOBA +*> \verbatim +*> JOBA is CHARACTER*1 +*> Specifies the level of accuracy: +*> = 'C': This option works well (high relative accuracy) if A = B * D, +*> with well-conditioned B and arbitrary diagonal matrix D. +*> The accuracy cannot be spoiled by COLUMN scaling. The +*> accuracy of the computed output depends on the condition of +*> B, and the procedure aims at the best theoretical accuracy. +*> The relative error max_{i=1:N}|d sigma_i| / sigma_i is +*> bounded by f(M,N)*epsilon* cond(B), independent of D. +*> The input matrix is preprocessed with the QRF with column +*> pivoting. This initial preprocessing and preconditioning by +*> a rank revealing QR factorization is common for all values of +*> JOBA. Additional actions are specified as follows: +*> = 'E': Computation as with 'C' with an additional estimate of the +*> condition number of B. It provides a realistic error bound. +*> = 'F': If A = D1 * C * D2 with ill-conditioned diagonal scalings +*> D1, D2, and well-conditioned matrix C, this option gives +*> higher accuracy than the 'C' option. If the structure of the +*> input matrix is not known, and relative accuracy is +*> desirable, then this option is advisable. The input matrix A +*> is preprocessed with QR factorization with FULL (row and +*> column) pivoting. +*> = 'G': Computation as with 'F' with an additional estimate of the +*> condition number of B, where A=D*B. If A has heavily weighted +*> rows, then using this condition number gives too pessimistic +*> error bound. +*> = 'A': Small singular values are the noise and the matrix is treated +*> as numerically rank deficient. The error in the computed +*> singular values is bounded by f(m,n)*epsilon*||A||. +*> The computed SVD A = U * S * V^t restores A up to +*> f(m,n)*epsilon*||A||. +*> This gives the procedure the licence to discard (set to zero) +*> all singular values below N*epsilon*||A||. +*> = 'R': Similar as in 'A'. Rank revealing property of the initial +*> QR factorization is used do reveal (using triangular factor) +*> a gap sigma_{r+1} < epsilon * sigma_r in which case the +*> numerical RANK is declared to be r. The SVD is computed with +*> absolute error bounds, but more accurately than with 'A'. +*> \endverbatim +*> +*> \param[in] JOBU +*> \verbatim +*> JOBU is CHARACTER*1 +*> Specifies whether to compute the columns of U: +*> = 'U': N columns of U are returned in the array U. +*> = 'F': full set of M left sing. vectors is returned in the array U. +*> = 'W': U may be used as workspace of length M*N. See the description +*> of U. +*> = 'N': U is not computed. +*> \endverbatim +*> +*> \param[in] JOBV +*> \verbatim +*> JOBV is CHARACTER*1 +*> Specifies whether to compute the matrix V: +*> = 'V': N columns of V are returned in the array V; Jacobi rotations +*> are not explicitly accumulated. +*> = 'J': N columns of V are returned in the array V, but they are +*> computed as the product of Jacobi rotations. This option is +*> allowed only if JOBU .NE. 'N', i.e. in computing the full SVD. +*> = 'W': V may be used as workspace of length N*N. See the description +*> of V. +*> = 'N': V is not computed. +*> \endverbatim +*> +*> \param[in] JOBR +*> \verbatim +*> JOBR is CHARACTER*1 +*> Specifies the RANGE for the singular values. Issues the licence to +*> set to zero small positive singular values if they are outside +*> specified range. If A .NE. 0 is scaled so that the largest singular +*> value of c*A is around DSQRT(BIG), BIG=SLAMCH('O'), then JOBR issues +*> the licence to kill columns of A whose norm in c*A is less than +*> DSQRT(SFMIN) (for JOBR = 'R'), or less than SMALL=SFMIN/EPSLN, +*> where SFMIN=SLAMCH('S'), EPSLN=SLAMCH('E'). +*> = 'N': Do not kill small columns of c*A. This option assumes that +*> BLAS and QR factorizations and triangular solvers are +*> implemented to work in that range. If the condition of A +*> is greater than BIG, use DGESVJ. +*> = 'R': RESTRICTED range for sigma(c*A) is [DSQRT(SFMIN), DSQRT(BIG)] +*> (roughly, as described above). This option is recommended. +*> ~~~~~~~~~~~~~~~~~~~~~~~~~~~ +*> For computing the singular values in the FULL range [SFMIN,BIG] +*> use DGESVJ. +*> \endverbatim +*> +*> \param[in] JOBT +*> \verbatim +*> JOBT is CHARACTER*1 +*> If the matrix is square then the procedure may determine to use +*> transposed A if A^t seems to be better with respect to convergence. +*> If the matrix is not square, JOBT is ignored. This is subject to +*> changes in the future. +*> The decision is based on two values of entropy over the adjoint +*> orbit of A^t * A. See the descriptions of WORK(6) and WORK(7). +*> = 'T': transpose if entropy test indicates possibly faster +*> convergence of Jacobi process if A^t is taken as input. If A is +*> replaced with A^t, then the row pivoting is included automatically. +*> = 'N': do not speculate. +*> This option can be used to compute only the singular values, or the +*> full SVD (U, SIGMA and V). For only one set of singular vectors +*> (U or V), the caller should provide both U and V, as one of the +*> matrices is used as workspace if the matrix A is transposed. +*> The implementer can easily remove this constraint and make the +*> code more complicated. See the descriptions of U and V. +*> \endverbatim +*> +*> \param[in] JOBP +*> \verbatim +*> JOBP is CHARACTER*1 +*> Issues the licence to introduce structured perturbations to drown +*> denormalized numbers. This licence should be active if the +*> denormals are poorly implemented, causing slow computation, +*> especially in cases of fast convergence (!). For details see [1,2]. +*> For the sake of simplicity, this perturbations are included only +*> when the full SVD or only the singular values are requested. The +*> implementer/user can easily add the perturbation for the cases of +*> computing one set of singular vectors. +*> = 'P': introduce perturbation +*> = 'N': do not perturb +*> \endverbatim +*> +*> \param[in] M +*> \verbatim +*> M is INTEGER +*> The number of rows of the input matrix A. M >= 0. +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of columns of the input matrix A. M >= N >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is DOUBLE PRECISION array, dimension (LDA,N) +*> On entry, the M-by-N matrix A. +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,M). +*> \endverbatim +*> +*> \param[out] SVA +*> \verbatim +*> SVA is DOUBLE PRECISION array, dimension (N) +*> On exit, +*> - For WORK(1)/WORK(2) = ONE: The singular values of A. During the +*> computation SVA contains Euclidean column norms of the +*> iterated matrices in the array A. +*> - For WORK(1) .NE. WORK(2): The singular values of A are +*> (WORK(1)/WORK(2)) * SVA(1:N). This factored form is used if +*> sigma_max(A) overflows or if small singular values have been +*> saved from underflow by scaling the input matrix A. +*> - If JOBR='R' then some of the singular values may be returned +*> as exact zeros obtained by "set to zero" because they are +*> below the numerical rank threshold or are denormalized numbers. +*> \endverbatim +*> +*> \param[out] U +*> \verbatim +*> U is DOUBLE PRECISION array, dimension ( LDU, N ) or ( LDU, M ) +*> If JOBU = 'U', then U contains on exit the M-by-N matrix of +*> the left singular vectors. +*> If JOBU = 'F', then U contains on exit the M-by-M matrix of +*> the left singular vectors, including an ONB +*> of the orthogonal complement of the Range(A). +*> If JOBU = 'W' .AND. (JOBV = 'V' .AND. JOBT = 'T' .AND. M = N), +*> then U is used as workspace if the procedure +*> replaces A with A^t. In that case, [V] is computed +*> in U as left singular vectors of A^t and then +*> copied back to the V array. This 'W' option is just +*> a reminder to the caller that in this case U is +*> reserved as workspace of length N*N. +*> If JOBU = 'N' U is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDU +*> \verbatim +*> LDU is INTEGER +*> The leading dimension of the array U, LDU >= 1. +*> IF JOBU = 'U' or 'F' or 'W', then LDU >= M. +*> \endverbatim +*> +*> \param[out] V +*> \verbatim +*> V is DOUBLE PRECISION array, dimension ( LDV, N ) +*> If JOBV = 'V', 'J' then V contains on exit the N-by-N matrix of +*> the right singular vectors; +*> If JOBV = 'W', AND (JOBU = 'U' AND JOBT = 'T' AND M = N), +*> then V is used as workspace if the pprocedure +*> replaces A with A^t. In that case, [U] is computed +*> in V as right singular vectors of A^t and then +*> copied back to the U array. This 'W' option is just +*> a reminder to the caller that in this case V is +*> reserved as workspace of length N*N. +*> If JOBV = 'N' V is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDV +*> \verbatim +*> LDV is INTEGER +*> The leading dimension of the array V, LDV >= 1. +*> If JOBV = 'V' or 'J' or 'W', then LDV >= N. +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is DOUBLE PRECISION array, dimension (MAX(7,LWORK)) +*> On exit, if N > 0 .AND. M > 0 (else not referenced), +*> WORK(1) = SCALE = WORK(2) / WORK(1) is the scaling factor such +*> that SCALE*SVA(1:N) are the computed singular values +*> of A. (See the description of SVA().) +*> WORK(2) = See the description of WORK(1). +*> WORK(3) = SCONDA is an estimate for the condition number of +*> column equilibrated A. (If JOBA = 'E' or 'G') +*> SCONDA is an estimate of DSQRT(||(R^t * R)^(-1)||_1). +*> It is computed using DPOCON. It holds +*> N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA +*> where R is the triangular factor from the QRF of A. +*> However, if R is truncated and the numerical rank is +*> determined to be strictly smaller than N, SCONDA is +*> returned as -1, thus indicating that the smallest +*> singular values might be lost. +*> +*> If full SVD is needed, the following two condition numbers are +*> useful for the analysis of the algorithm. They are provided for +*> a developer/implementer who is familiar with the details of +*> the method. +*> +*> WORK(4) = an estimate of the scaled condition number of the +*> triangular factor in the first QR factorization. +*> WORK(5) = an estimate of the scaled condition number of the +*> triangular factor in the second QR factorization. +*> The following two parameters are computed if JOBT = 'T'. +*> They are provided for a developer/implementer who is familiar +*> with the details of the method. +*> +*> WORK(6) = the entropy of A^t*A :: this is the Shannon entropy +*> of diag(A^t*A) / Trace(A^t*A) taken as point in the +*> probability simplex. +*> WORK(7) = the entropy of A*A^t. +*> \endverbatim +*> +*> \param[in] LWORK +*> \verbatim +*> LWORK is INTEGER +*> Length of WORK to confirm proper allocation of work space. +*> LWORK depends on the job: +*> +*> If only SIGMA is needed (JOBU = 'N', JOBV = 'N') and +*> -> .. no scaled condition estimate required (JOBE = 'N'): +*> LWORK >= max(2*M+N,4*N+1,7). This is the minimal requirement. +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= max(2*M+N,3*N+(N+1)*NB,7). Here NB is the optimal +*> block size for DGEQP3 and DGEQRF. +*> In general, optimal LWORK is computed as +*> LWORK >= max(2*M+N,N+LWORK(DGEQP3),N+LWORK(DGEQRF), 7). +*> -> .. an estimate of the scaled condition number of A is +*> required (JOBA='E', 'G'). In this case, LWORK is the maximum +*> of the above and N*N+4*N, i.e. LWORK >= max(2*M+N,N*N+4*N,7). +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= max(2*M+N,3*N+(N+1)*NB, N*N+4*N, 7). +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(2*M+N,N+LWORK(DGEQP3),N+LWORK(DGEQRF), +*> N+N*N+LWORK(DPOCON),7). +*> +*> If SIGMA and the right singular vectors are needed (JOBV = 'V'), +*> -> the minimal requirement is LWORK >= max(2*M+N,4*N+1,7). +*> -> For optimal performance, LWORK >= max(2*M+N,3*N+(N+1)*NB,7), +*> where NB is the optimal block size for DGEQP3, DGEQRF, DGELQF, +*> DORMLQ. In general, the optimal length LWORK is computed as +*> LWORK >= max(2*M+N,N+LWORK(DGEQP3), N+LWORK(DPOCON), +*> N+LWORK(DGELQF), 2*N+LWORK(DGEQRF), N+LWORK(DORMLQ)). +*> +*> If SIGMA and the left singular vectors are needed +*> -> the minimal requirement is LWORK >= max(2*M+N,4*N+1,7). +*> -> For optimal performance: +*> if JOBU = 'U' :: LWORK >= max(2*M+N,3*N+(N+1)*NB,7), +*> if JOBU = 'F' :: LWORK >= max(2*M+N,3*N+(N+1)*NB,N+M*NB,7), +*> where NB is the optimal block size for DGEQP3, DGEQRF, DORMQR. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(2*M+N,N+LWORK(DGEQP3),N+LWORK(DPOCON), +*> 2*N+LWORK(DGEQRF), N+LWORK(DORMQR)). +*> Here LWORK(DORMQR) equals N*NB (for JOBU = 'U') or +*> M*NB (for JOBU = 'F'). +*> +*> If the full SVD is needed: (JOBU = 'U' or JOBU = 'F') and +*> -> if JOBV = 'V' +*> the minimal requirement is LWORK >= max(2*M+N,6*N+2*N*N). +*> -> if JOBV = 'J' the minimal requirement is +*> LWORK >= max(2*M+N, 4*N+N*N,2*N+N*N+6). +*> -> For optimal performance, LWORK should be additionally +*> larger than N+M*NB, where NB is the optimal block size +*> for DORMQR. +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, dimension (MAX(3,M+3*N)). +*> On exit, +*> IWORK(1) = the numerical rank determined after the initial +*> QR factorization with pivoting. See the descriptions +*> of JOBA and JOBR. +*> IWORK(2) = the number of the computed nonzero singular values +*> IWORK(3) = if nonzero, a warning message: +*> If IWORK(3) = 1 then some of the column norms of A +*> were denormalized floats. The requested high accuracy +*> is not warranted by the data. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> < 0: if INFO = -i, then the i-th argument had an illegal value. +*> = 0: successful exit; +*> > 0: DGEJSV did not converge in the maximal allowed number +*> of sweeps. The computed values may be inaccurate. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup doubleGEsing +* +*> \par Further Details: +* ===================== +*> +*> \verbatim +*> +*> DGEJSV implements a preconditioned Jacobi SVD algorithm. It uses DGEQP3, +*> DGEQRF, and DGELQF as preprocessors and preconditioners. Optionally, an +*> additional row pivoting can be used as a preprocessor, which in some +*> cases results in much higher accuracy. An example is matrix A with the +*> structure A = D1 * C * D2, where D1, D2 are arbitrarily ill-conditioned +*> diagonal matrices and C is well-conditioned matrix. In that case, complete +*> pivoting in the first QR factorizations provides accuracy dependent on the +*> condition number of C, and independent of D1, D2. Such higher accuracy is +*> not completely understood theoretically, but it works well in practice. +*> Further, if A can be written as A = B*D, with well-conditioned B and some +*> diagonal D, then the high accuracy is guaranteed, both theoretically and +*> in software, independent of D. For more details see [1], [2]. +*> The computational range for the singular values can be the full range +*> ( UNDERFLOW,OVERFLOW ), provided that the machine arithmetic and the BLAS +*> & LAPACK routines called by DGEJSV are implemented to work in that range. +*> If that is not the case, then the restriction for safe computation with +*> the singular values in the range of normalized IEEE numbers is that the +*> spectral condition number kappa(A)=sigma_max(A)/sigma_min(A) does not +*> overflow. This code (DGEJSV) is best used in this restricted range, +*> meaning that singular values of magnitude below ||A||_2 / DLAMCH('O') are +*> returned as zeros. See JOBR for details on this. +*> Further, this implementation is somewhat slower than the one described +*> in [1,2] due to replacement of some non-LAPACK components, and because +*> the choice of some tuning parameters in the iterative part (DGESVJ) is +*> left to the implementer on a particular machine. +*> The rank revealing QR factorization (in this code: DGEQP3) should be +*> implemented as in [3]. We have a new version of DGEQP3 under development +*> that is more robust than the current one in LAPACK, with a cleaner cut in +*> rank deficient cases. It will be available in the SIGMA library [4]. +*> If M is much larger than N, it is obvious that the initial QRF with +*> column pivoting can be preprocessed by the QRF without pivoting. That +*> well known trick is not used in DGEJSV because in some cases heavy row +*> weighting can be treated with complete pivoting. The overhead in cases +*> M much larger than N is then only due to pivoting, but the benefits in +*> terms of accuracy have prevailed. The implementer/user can incorporate +*> this extra QRF step easily. The implementer can also improve data movement +*> (matrix transpose, matrix copy, matrix transposed copy) - this +*> implementation of DGEJSV uses only the simplest, naive data movement. +*> \endverbatim +* +*> \par Contributors: +* ================== +*> +*> Zlatko Drmac (Zagreb, Croatia) and Kresimir Veselic (Hagen, Germany) +* +*> \par References: +* ================ +*> +*> \verbatim +*> +*> [1] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. +*> LAPACK Working note 169. +*> [2] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. +*> LAPACK Working note 170. +*> [3] Z. Drmac and Z. Bujanovic: On the failure of rank-revealing QR +*> factorization software - a case study. +*> ACM Trans. Math. Softw. Vol. 35, No 2 (2008), pp. 1-28. +*> LAPACK Working note 176. +*> [4] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, +*> QSVD, (H,K)-SVD computations. +*> Department of Mathematics, University of Zagreb, 2008. +*> \endverbatim +* +*> \par Bugs, examples and comments: +* ================================= +*> +*> Please report all bugs and send interesting examples and/or comments to +*> drmac@math.hr. Thank you. +*> +* ===================================================================== + SUBROUTINE DGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, + $ M, N, A, LDA, SVA, U, LDU, V, LDV, + $ WORK, LWORK, IWORK, INFO ) +* +* -- LAPACK computational routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + IMPLICIT NONE + INTEGER INFO, LDA, LDU, LDV, LWORK, M, N +* .. +* .. Array Arguments .. + DOUBLE PRECISION A( LDA, * ), SVA( N ), U( LDU, * ), V( LDV, * ), + $ WORK( LWORK ) + INTEGER IWORK( * ) + CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* =========================================================================== +* +* .. Local Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D0, ONE = 1.0D0 ) +* .. +* .. Local Scalars .. + DOUBLE PRECISION AAPP, AAQQ, AATMAX, AATMIN, BIG, BIG1, COND_OK, + $ CONDR1, CONDR2, ENTRA, ENTRAT, EPSLN, MAXPRJ, SCALEM, + $ SCONDA, SFMIN, SMALL, TEMP1, USCAL1, USCAL2, XSC + INTEGER IERR, N1, NR, NUMRANK, p, q, WARNING + LOGICAL ALMORT, DEFR, ERREST, GOSCAL, JRACC, KILL, LSVEC, + $ L2ABER, L2KILL, L2PERT, L2RANK, L2TRAN, + $ NOSCAL, ROWPIV, RSVEC, TRANSP +* .. +* .. Intrinsic Functions .. + INTRINSIC DABS, DLOG, MAX, MIN, DBLE, IDNINT, DSIGN, DSQRT +* .. +* .. External Functions .. + DOUBLE PRECISION DLAMCH, DNRM2 + INTEGER IDAMAX + LOGICAL LSAME + EXTERNAL IDAMAX, LSAME, DLAMCH, DNRM2 +* .. +* .. External Subroutines .. + EXTERNAL DCOPY, DGELQF, DGEQP3, DGEQRF, DLACPY, DLASCL, + $ DLASET, DLASSQ, DLASWP, DORGQR, DORMLQ, + $ DORMQR, DPOCON, DSCAL, DSWAP, DTRSM, XERBLA +* + EXTERNAL DGESVJ +* .. +* +* Test the input arguments +* + LSVEC = LSAME( JOBU, 'U' ) .OR. LSAME( JOBU, 'F' ) + JRACC = LSAME( JOBV, 'J' ) + RSVEC = LSAME( JOBV, 'V' ) .OR. JRACC + ROWPIV = LSAME( JOBA, 'F' ) .OR. LSAME( JOBA, 'G' ) + L2RANK = LSAME( JOBA, 'R' ) + L2ABER = LSAME( JOBA, 'A' ) + ERREST = LSAME( JOBA, 'E' ) .OR. LSAME( JOBA, 'G' ) + L2TRAN = LSAME( JOBT, 'T' ) + L2KILL = LSAME( JOBR, 'R' ) + DEFR = LSAME( JOBR, 'N' ) + L2PERT = LSAME( JOBP, 'P' ) +* + IF ( .NOT.(ROWPIV .OR. L2RANK .OR. L2ABER .OR. + $ ERREST .OR. LSAME( JOBA, 'C' ) )) THEN + INFO = - 1 + ELSE IF ( .NOT.( LSVEC .OR. LSAME( JOBU, 'N' ) .OR. + $ LSAME( JOBU, 'W' )) ) THEN + INFO = - 2 + ELSE IF ( .NOT.( RSVEC .OR. LSAME( JOBV, 'N' ) .OR. + $ LSAME( JOBV, 'W' )) .OR. ( JRACC .AND. (.NOT.LSVEC) ) ) THEN + INFO = - 3 + ELSE IF ( .NOT. ( L2KILL .OR. DEFR ) ) THEN + INFO = - 4 + ELSE IF ( .NOT. ( L2TRAN .OR. LSAME( JOBT, 'N' ) ) ) THEN + INFO = - 5 + ELSE IF ( .NOT. ( L2PERT .OR. LSAME( JOBP, 'N' ) ) ) THEN + INFO = - 6 + ELSE IF ( M .LT. 0 ) THEN + INFO = - 7 + ELSE IF ( ( N .LT. 0 ) .OR. ( N .GT. M ) ) THEN + INFO = - 8 + ELSE IF ( LDA .LT. M ) THEN + INFO = - 10 + ELSE IF ( LSVEC .AND. ( LDU .LT. M ) ) THEN + INFO = - 13 + ELSE IF ( RSVEC .AND. ( LDV .LT. N ) ) THEN + INFO = - 15 + ELSE IF ( (.NOT.(LSVEC .OR. RSVEC .OR. ERREST).AND. + & (LWORK .LT. MAX(7,4*N+1,2*M+N))) .OR. + & (.NOT.(LSVEC .OR. RSVEC) .AND. ERREST .AND. + & (LWORK .LT. MAX(7,4*N+N*N,2*M+N))) .OR. + & (LSVEC .AND. (.NOT.RSVEC) .AND. (LWORK .LT. MAX(7,2*M+N,4*N+1))) + & .OR. + & (RSVEC .AND. (.NOT.LSVEC) .AND. (LWORK .LT. MAX(7,2*M+N,4*N+1))) + & .OR. + & (LSVEC .AND. RSVEC .AND. (.NOT.JRACC) .AND. + & (LWORK.LT.MAX(2*M+N,6*N+2*N*N))) + & .OR. (LSVEC .AND. RSVEC .AND. JRACC .AND. + & LWORK.LT.MAX(2*M+N,4*N+N*N,2*N+N*N+6))) + & THEN + INFO = - 17 + ELSE +* #:) + INFO = 0 + END IF +* + IF ( INFO .NE. 0 ) THEN +* #:( + CALL XERBLA( 'DGEJSV', - INFO ) + RETURN + END IF +* +* Quick return for void matrix (Y3K safe) +* #:) + IF ( ( M .EQ. 0 ) .OR. ( N .EQ. 0 ) ) THEN + IWORK(1:3) = 0 + WORK(1:7) = 0 + RETURN + ENDIF +* +* Determine whether the matrix U should be M x N or M x M +* + IF ( LSVEC ) THEN + N1 = N + IF ( LSAME( JOBU, 'F' ) ) N1 = M + END IF +* +* Set numerical parameters +* +*! NOTE: Make sure DLAMCH() does not fail on the target architecture. +* + EPSLN = DLAMCH('Epsilon') + SFMIN = DLAMCH('SafeMinimum') + SMALL = SFMIN / EPSLN + BIG = DLAMCH('O') +* BIG = ONE / SFMIN +* +* Initialize SVA(1:N) = diag( ||A e_i||_2 )_1^N +* +*(!) If necessary, scale SVA() to protect the largest norm from +* overflow. It is possible that this scaling pushes the smallest +* column norm left from the underflow threshold (extreme case). +* + SCALEM = ONE / DSQRT(DBLE(M)*DBLE(N)) + NOSCAL = .TRUE. + GOSCAL = .TRUE. + DO 1874 p = 1, N + AAPP = ZERO + AAQQ = ONE + CALL DLASSQ( M, A(1,p), 1, AAPP, AAQQ ) + IF ( AAPP .GT. BIG ) THEN + INFO = - 9 + CALL XERBLA( 'DGEJSV', -INFO ) + RETURN + END IF + AAQQ = DSQRT(AAQQ) + IF ( ( AAPP .LT. (BIG / AAQQ) ) .AND. NOSCAL ) THEN + SVA(p) = AAPP * AAQQ + ELSE + NOSCAL = .FALSE. + SVA(p) = AAPP * ( AAQQ * SCALEM ) + IF ( GOSCAL ) THEN + GOSCAL = .FALSE. + CALL DSCAL( p-1, SCALEM, SVA, 1 ) + END IF + END IF + 1874 CONTINUE +* + IF ( NOSCAL ) SCALEM = ONE +* + AAPP = ZERO + AAQQ = BIG + DO 4781 p = 1, N + AAPP = MAX( AAPP, SVA(p) ) + IF ( SVA(p) .NE. ZERO ) AAQQ = MIN( AAQQ, SVA(p) ) + 4781 CONTINUE +* +* Quick return for zero M x N matrix +* #:) + IF ( AAPP .EQ. ZERO ) THEN + IF ( LSVEC ) CALL DLASET( 'G', M, N1, ZERO, ONE, U, LDU ) + IF ( RSVEC ) CALL DLASET( 'G', N, N, ZERO, ONE, V, LDV ) + WORK(1) = ONE + WORK(2) = ONE + IF ( ERREST ) WORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + WORK(4) = ONE + WORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + WORK(6) = ZERO + WORK(7) = ZERO + END IF + IWORK(1) = 0 + IWORK(2) = 0 + IWORK(3) = 0 + RETURN + END IF +* +* Issue warning if denormalized column norms detected. Override the +* high relative accuracy request. Issue licence to kill columns +* (set them to zero) whose norm is less than sigma_max / BIG (roughly). +* #:( + WARNING = 0 + IF ( AAQQ .LE. SFMIN ) THEN + L2RANK = .TRUE. + L2KILL = .TRUE. + WARNING = 1 + END IF +* +* Quick return for one-column matrix +* #:) + IF ( N .EQ. 1 ) THEN +* + IF ( LSVEC ) THEN + CALL DLASCL( 'G',0,0,SVA(1),SCALEM, M,1,A(1,1),LDA,IERR ) + CALL DLACPY( 'A', M, 1, A, LDA, U, LDU ) +* computing all M left singular vectors of the M x 1 matrix + IF ( N1 .NE. N ) THEN + CALL DGEQRF( M, N, U,LDU, WORK, WORK(N+1),LWORK-N,IERR ) + CALL DORGQR( M,N1,1, U,LDU,WORK,WORK(N+1),LWORK-N,IERR ) + CALL DCOPY( M, A(1,1), 1, U(1,1), 1 ) + END IF + END IF + IF ( RSVEC ) THEN + V(1,1) = ONE + END IF + IF ( SVA(1) .LT. (BIG*SCALEM) ) THEN + SVA(1) = SVA(1) / SCALEM + SCALEM = ONE + END IF + WORK(1) = ONE / SCALEM + WORK(2) = ONE + IF ( SVA(1) .NE. ZERO ) THEN + IWORK(1) = 1 + IF ( ( SVA(1) / SCALEM) .GE. SFMIN ) THEN + IWORK(2) = 1 + ELSE + IWORK(2) = 0 + END IF + ELSE + IWORK(1) = 0 + IWORK(2) = 0 + END IF + IWORK(3) = 0 + IF ( ERREST ) WORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + WORK(4) = ONE + WORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + WORK(6) = ZERO + WORK(7) = ZERO + END IF + RETURN +* + END IF +* + TRANSP = .FALSE. + L2TRAN = L2TRAN .AND. ( M .EQ. N ) +* + AATMAX = -ONE + AATMIN = BIG + IF ( ROWPIV .OR. L2TRAN ) THEN +* +* Compute the row norms, needed to determine row pivoting sequence +* (in the case of heavily row weighted A, row pivoting is strongly +* advised) and to collect information needed to compare the +* structures of A * A^t and A^t * A (in the case L2TRAN.EQ..TRUE.). +* + IF ( L2TRAN ) THEN + DO 1950 p = 1, M + XSC = ZERO + TEMP1 = ONE + CALL DLASSQ( N, A(p,1), LDA, XSC, TEMP1 ) +* DLASSQ gets both the ell_2 and the ell_infinity norm +* in one pass through the vector + WORK(M+N+p) = XSC * SCALEM + WORK(N+p) = XSC * (SCALEM*DSQRT(TEMP1)) + AATMAX = MAX( AATMAX, WORK(N+p) ) + IF (WORK(N+p) .NE. ZERO) AATMIN = MIN(AATMIN,WORK(N+p)) + 1950 CONTINUE + ELSE + DO 1904 p = 1, M + WORK(M+N+p) = SCALEM*DABS( A(p,IDAMAX(N,A(p,1),LDA)) ) + AATMAX = MAX( AATMAX, WORK(M+N+p) ) + AATMIN = MIN( AATMIN, WORK(M+N+p) ) + 1904 CONTINUE + END IF +* + END IF +* +* For square matrix A try to determine whether A^t would be better +* input for the preconditioned Jacobi SVD, with faster convergence. +* The decision is based on an O(N) function of the vector of column +* and row norms of A, based on the Shannon entropy. This should give +* the right choice in most cases when the difference actually matters. +* It may fail and pick the slower converging side. +* + ENTRA = ZERO + ENTRAT = ZERO + IF ( L2TRAN ) THEN +* + XSC = ZERO + TEMP1 = ONE + CALL DLASSQ( N, SVA, 1, XSC, TEMP1 ) + TEMP1 = ONE / TEMP1 +* + ENTRA = ZERO + DO 1113 p = 1, N + BIG1 = ( ( SVA(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRA = ENTRA + BIG1 * DLOG(BIG1) + 1113 CONTINUE + ENTRA = - ENTRA / DLOG(DBLE(N)) +* +* Now, SVA().^2/Trace(A^t * A) is a point in the probability simplex. +* It is derived from the diagonal of A^t * A. Do the same with the +* diagonal of A * A^t, compute the entropy of the corresponding +* probability distribution. Note that A * A^t and A^t * A have the +* same trace. +* + ENTRAT = ZERO + DO 1114 p = N+1, N+M + BIG1 = ( ( WORK(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRAT = ENTRAT + BIG1 * DLOG(BIG1) + 1114 CONTINUE + ENTRAT = - ENTRAT / DLOG(DBLE(M)) +* +* Analyze the entropies and decide A or A^t. Smaller entropy +* usually means better input for the algorithm. +* + TRANSP = ( ENTRAT .LT. ENTRA ) +* +* If A^t is better than A, transpose A. +* + IF ( TRANSP ) THEN +* In an optimal implementation, this trivial transpose +* should be replaced with faster transpose. + DO 1115 p = 1, N - 1 + DO 1116 q = p + 1, N + TEMP1 = A(q,p) + A(q,p) = A(p,q) + A(p,q) = TEMP1 + 1116 CONTINUE + 1115 CONTINUE + DO 1117 p = 1, N + WORK(M+N+p) = SVA(p) + SVA(p) = WORK(N+p) + 1117 CONTINUE + TEMP1 = AAPP + AAPP = AATMAX + AATMAX = TEMP1 + TEMP1 = AAQQ + AAQQ = AATMIN + AATMIN = TEMP1 + KILL = LSVEC + LSVEC = RSVEC + RSVEC = KILL + IF ( LSVEC ) N1 = N +* + ROWPIV = .TRUE. + END IF +* + END IF +* END IF L2TRAN +* +* Scale the matrix so that its maximal singular value remains less +* than DSQRT(BIG) -- the matrix is scaled so that its maximal column +* has Euclidean norm equal to DSQRT(BIG/N). The only reason to keep +* DSQRT(BIG) instead of BIG is the fact that DGEJSV uses LAPACK and +* BLAS routines that, in some implementations, are not capable of +* working in the full interval [SFMIN,BIG] and that they may provoke +* overflows in the intermediate results. If the singular values spread +* from SFMIN to BIG, then DGESVJ will compute them. So, in that case, +* one should use DGESVJ instead of DGEJSV. +* + BIG1 = DSQRT( BIG ) + TEMP1 = DSQRT( BIG / DBLE(N) ) +* + CALL DLASCL( 'G', 0, 0, AAPP, TEMP1, N, 1, SVA, N, IERR ) + IF ( AAQQ .GT. (AAPP * SFMIN) ) THEN + AAQQ = ( AAQQ / AAPP ) * TEMP1 + ELSE + AAQQ = ( AAQQ * TEMP1 ) / AAPP + END IF + TEMP1 = TEMP1 * SCALEM + CALL DLASCL( 'G', 0, 0, AAPP, TEMP1, M, N, A, LDA, IERR ) +* +* To undo scaling at the end of this procedure, multiply the +* computed singular values with USCAL2 / USCAL1. +* + USCAL1 = TEMP1 + USCAL2 = AAPP +* + IF ( L2KILL ) THEN +* L2KILL enforces computation of nonzero singular values in +* the restricted range of condition number of the initial A, +* sigma_max(A) / sigma_min(A) approx. DSQRT(BIG)/DSQRT(SFMIN). + XSC = DSQRT( SFMIN ) + ELSE + XSC = SMALL +* +* Now, if the condition number of A is too big, +* sigma_max(A) / sigma_min(A) .GT. DSQRT(BIG/N) * EPSLN / SFMIN, +* as a precaution measure, the full SVD is computed using DGESVJ +* with accumulated Jacobi rotations. This provides numerically +* more robust computation, at the cost of slightly increased run +* time. Depending on the concrete implementation of BLAS and LAPACK +* (i.e. how they behave in presence of extreme ill-conditioning) the +* implementor may decide to remove this switch. + IF ( ( AAQQ.LT.DSQRT(SFMIN) ) .AND. LSVEC .AND. RSVEC ) THEN + JRACC = .TRUE. + END IF +* + END IF + IF ( AAQQ .LT. XSC ) THEN + DO 700 p = 1, N + IF ( SVA(p) .LT. XSC ) THEN + CALL DLASET( 'A', M, 1, ZERO, ZERO, A(1,p), LDA ) + SVA(p) = ZERO + END IF + 700 CONTINUE + END IF +* +* Preconditioning using QR factorization with pivoting +* + IF ( ROWPIV ) THEN +* Optional row permutation (Bjoerck row pivoting): +* A result by Cox and Higham shows that the Bjoerck's +* row pivoting combined with standard column pivoting +* has similar effect as Powell-Reid complete pivoting. +* The ell-infinity norms of A are made nonincreasing. + DO 1952 p = 1, M - 1 + q = IDAMAX( M-p+1, WORK(M+N+p), 1 ) + p - 1 + IWORK(2*N+p) = q + IF ( p .NE. q ) THEN + TEMP1 = WORK(M+N+p) + WORK(M+N+p) = WORK(M+N+q) + WORK(M+N+q) = TEMP1 + END IF + 1952 CONTINUE + CALL DLASWP( N, A, LDA, 1, M-1, IWORK(2*N+1), 1 ) + END IF +* +* End of the preparation phase (scaling, optional sorting and +* transposing, optional flushing of small columns). +* +* Preconditioning +* +* If the full SVD is needed, the right singular vectors are computed +* from a matrix equation, and for that we need theoretical analysis +* of the Businger-Golub pivoting. So we use DGEQP3 as the first RR QRF. +* In all other cases the first RR QRF can be chosen by other criteria +* (eg speed by replacing global with restricted window pivoting, such +* as in SGEQPX from TOMS # 782). Good results will be obtained using +* SGEQPX with properly (!) chosen numerical parameters. +* Any improvement of DGEQP3 improves overall performance of DGEJSV. +* +* A * P1 = Q1 * [ R1^t 0]^t: + DO 1963 p = 1, N +* .. all columns are free columns + IWORK(p) = 0 + 1963 CONTINUE + CALL DGEQP3( M,N,A,LDA, IWORK,WORK, WORK(N+1),LWORK-N, IERR ) +* +* The upper triangular matrix R1 from the first QRF is inspected for +* rank deficiency and possibilities for deflation, or possible +* ill-conditioning. Depending on the user specified flag L2RANK, +* the procedure explores possibilities to reduce the numerical +* rank by inspecting the computed upper triangular factor. If +* L2RANK or L2ABER are up, then DGEJSV will compute the SVD of +* A + dA, where ||dA|| <= f(M,N)*EPSLN. +* + NR = 1 + IF ( L2ABER ) THEN +* Standard absolute error bound suffices. All sigma_i with +* sigma_i < N*EPSLN*||A|| are flushed to zero. This is an +* aggressive enforcement of lower numerical rank by introducing a +* backward error of the order of N*EPSLN*||A||. + TEMP1 = DSQRT(DBLE(N))*EPSLN + DO 3001 p = 2, N + IF ( DABS(A(p,p)) .GE. (TEMP1*DABS(A(1,1))) ) THEN + NR = NR + 1 + ELSE + GO TO 3002 + END IF + 3001 CONTINUE + 3002 CONTINUE + ELSE IF ( L2RANK ) THEN +* .. similarly as above, only slightly more gentle (less aggressive). +* Sudden drop on the diagonal of R1 is used as the criterion for +* close-to-rank-deficient. + TEMP1 = DSQRT(SFMIN) + DO 3401 p = 2, N + IF ( ( DABS(A(p,p)) .LT. (EPSLN*DABS(A(p-1,p-1))) ) .OR. + $ ( DABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (DABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3402 + NR = NR + 1 + 3401 CONTINUE + 3402 CONTINUE +* + ELSE +* The goal is high relative accuracy. However, if the matrix +* has high scaled condition number the relative accuracy is in +* general not feasible. Later on, a condition number estimator +* will be deployed to estimate the scaled condition number. +* Here we just remove the underflowed part of the triangular +* factor. This prevents the situation in which the code is +* working hard to get the accuracy not warranted by the data. + TEMP1 = DSQRT(SFMIN) + DO 3301 p = 2, N + IF ( ( DABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (DABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3302 + NR = NR + 1 + 3301 CONTINUE + 3302 CONTINUE +* + END IF +* + ALMORT = .FALSE. + IF ( NR .EQ. N ) THEN + MAXPRJ = ONE + DO 3051 p = 2, N + TEMP1 = DABS(A(p,p)) / SVA(IWORK(p)) + MAXPRJ = MIN( MAXPRJ, TEMP1 ) + 3051 CONTINUE + IF ( MAXPRJ**2 .GE. ONE - DBLE(N)*EPSLN ) ALMORT = .TRUE. + END IF +* +* + SCONDA = - ONE + CONDR1 = - ONE + CONDR2 = - ONE +* + IF ( ERREST ) THEN + IF ( N .EQ. NR ) THEN + IF ( RSVEC ) THEN +* .. V is available as workspace + CALL DLACPY( 'U', N, N, A, LDA, V, LDV ) + DO 3053 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL DSCAL( p, ONE/TEMP1, V(1,p), 1 ) + 3053 CONTINUE + CALL DPOCON( 'U', N, V, LDV, ONE, TEMP1, + $ WORK(N+1), IWORK(2*N+M+1), IERR ) + ELSE IF ( LSVEC ) THEN +* .. U is available as workspace + CALL DLACPY( 'U', N, N, A, LDA, U, LDU ) + DO 3054 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL DSCAL( p, ONE/TEMP1, U(1,p), 1 ) + 3054 CONTINUE + CALL DPOCON( 'U', N, U, LDU, ONE, TEMP1, + $ WORK(N+1), IWORK(2*N+M+1), IERR ) + ELSE + CALL DLACPY( 'U', N, N, A, LDA, WORK(N+1), N ) + DO 3052 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL DSCAL( p, ONE/TEMP1, WORK(N+(p-1)*N+1), 1 ) + 3052 CONTINUE +* .. the columns of R are scaled to have unit Euclidean lengths. + CALL DPOCON( 'U', N, WORK(N+1), N, ONE, TEMP1, + $ WORK(N+N*N+1), IWORK(2*N+M+1), IERR ) + END IF + SCONDA = ONE / DSQRT(TEMP1) +* SCONDA is an estimate of DSQRT(||(R^t * R)^(-1)||_1). +* N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA + ELSE + SCONDA = - ONE + END IF + END IF +* + L2PERT = L2PERT .AND. ( DABS( A(1,1)/A(NR,NR) ) .GT. DSQRT(BIG1) ) +* If there is no violent scaling, artificial perturbation is not needed. +* +* Phase 3: +* + IF ( .NOT. ( RSVEC .OR. LSVEC ) ) THEN +* +* Singular Values only +* +* .. transpose A(1:NR,1:N) + DO 1946 p = 1, MIN( N-1, NR ) + CALL DCOPY( N-p, A(p,p+1), LDA, A(p+1,p), 1 ) + 1946 CONTINUE +* +* The following two DO-loops introduce small relative perturbation +* into the strict upper triangle of the lower triangular matrix. +* Small entries below the main diagonal are also changed. +* This modification is useful if the computing environment does not +* provide/allow FLUSH TO ZERO underflow, for it prevents many +* annoying denormalized numbers in case of strongly scaled matrices. +* The perturbation is structured so that it does not introduce any +* new perturbation of the singular values, and it does not destroy +* the job done by the preconditioner. +* The licence for this perturbation is in the variable L2PERT, which +* should be .FALSE. if FLUSH TO ZERO underflow is active. +* + IF ( .NOT. ALMORT ) THEN +* + IF ( L2PERT ) THEN +* XSC = DSQRT(SMALL) + XSC = EPSLN / DBLE(N) + DO 4947 q = 1, NR + TEMP1 = XSC*DABS(A(q,q)) + DO 4949 p = 1, N + IF ( ( (p.GT.q) .AND. (DABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) + $ A(p,q) = DSIGN( TEMP1, A(p,q) ) + 4949 CONTINUE + 4947 CONTINUE + ELSE + CALL DLASET( 'U', NR-1,NR-1, ZERO,ZERO, A(1,2),LDA ) + END IF +* +* .. second preconditioning using the QR factorization +* + CALL DGEQRF( N,NR, A,LDA, WORK, WORK(N+1),LWORK-N, IERR ) +* +* .. and transpose upper to lower triangular + DO 1948 p = 1, NR - 1 + CALL DCOPY( NR-p, A(p,p+1), LDA, A(p+1,p), 1 ) + 1948 CONTINUE +* + END IF +* +* Row-cyclic Jacobi SVD algorithm with column pivoting +* +* .. again some perturbation (a "background noise") is added +* to drown denormals + IF ( L2PERT ) THEN +* XSC = DSQRT(SMALL) + XSC = EPSLN / DBLE(N) + DO 1947 q = 1, NR + TEMP1 = XSC*DABS(A(q,q)) + DO 1949 p = 1, NR + IF ( ( (p.GT.q) .AND. (DABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) + $ A(p,q) = DSIGN( TEMP1, A(p,q) ) + 1949 CONTINUE + 1947 CONTINUE + ELSE + CALL DLASET( 'U', NR-1, NR-1, ZERO, ZERO, A(1,2), LDA ) + END IF +* +* .. and one-sided Jacobi rotations are started on a lower +* triangular matrix (plus perturbation which is ignored in +* the part which destroys triangular form (confusing?!)) +* + CALL DGESVJ( 'L', 'NoU', 'NoV', NR, NR, A, LDA, SVA, + $ N, V, LDV, WORK, LWORK, INFO ) +* + SCALEM = WORK(1) + NUMRANK = IDNINT(WORK(2)) +* +* + ELSE IF ( RSVEC .AND. ( .NOT. LSVEC ) ) THEN +* +* -> Singular Values and Right Singular Vectors <- +* + IF ( ALMORT ) THEN +* +* .. in this case NR equals N + DO 1998 p = 1, NR + CALL DCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + 1998 CONTINUE + CALL DLASET( 'Upper', NR-1, NR-1, ZERO, ZERO, V(1,2), LDV ) +* + CALL DGESVJ( 'L','U','N', N, NR, V,LDV, SVA, NR, A,LDA, + $ WORK, LWORK, INFO ) + SCALEM = WORK(1) + NUMRANK = IDNINT(WORK(2)) + + ELSE +* +* .. two more QR factorizations ( one QRF is not enough, two require +* accumulated product of Jacobi rotations, three are perfect ) +* + CALL DLASET( 'Lower', NR-1, NR-1, ZERO, ZERO, A(2,1), LDA ) + CALL DGELQF( NR, N, A, LDA, WORK, WORK(N+1), LWORK-N, IERR) + CALL DLACPY( 'Lower', NR, NR, A, LDA, V, LDV ) + CALL DLASET( 'Upper', NR-1, NR-1, ZERO, ZERO, V(1,2), LDV ) + CALL DGEQRF( NR, NR, V, LDV, WORK(N+1), WORK(2*N+1), + $ LWORK-2*N, IERR ) + DO 8998 p = 1, NR + CALL DCOPY( NR-p+1, V(p,p), LDV, V(p,p), 1 ) + 8998 CONTINUE + CALL DLASET( 'Upper', NR-1, NR-1, ZERO, ZERO, V(1,2), LDV ) +* + CALL DGESVJ( 'Lower', 'U','N', NR, NR, V,LDV, SVA, NR, U, + $ LDU, WORK(N+1), LWORK, INFO ) + SCALEM = WORK(N+1) + NUMRANK = IDNINT(WORK(N+2)) + IF ( NR .LT. N ) THEN + CALL DLASET( 'A',N-NR, NR, ZERO,ZERO, V(NR+1,1), LDV ) + CALL DLASET( 'A',NR, N-NR, ZERO,ZERO, V(1,NR+1), LDV ) + CALL DLASET( 'A',N-NR,N-NR,ZERO,ONE, V(NR+1,NR+1), LDV ) + END IF +* + CALL DORMLQ( 'Left', 'Transpose', N, N, NR, A, LDA, WORK, + $ V, LDV, WORK(N+1), LWORK-N, IERR ) +* + END IF +* + DO 8991 p = 1, N + CALL DCOPY( N, V(p,1), LDV, A(IWORK(p),1), LDA ) + 8991 CONTINUE + CALL DLACPY( 'All', N, N, A, LDA, V, LDV ) +* + IF ( TRANSP ) THEN + CALL DLACPY( 'All', N, N, V, LDV, U, LDU ) + END IF +* + ELSE IF ( LSVEC .AND. ( .NOT. RSVEC ) ) THEN +* +* .. Singular Values and Left Singular Vectors .. +* +* .. second preconditioning step to avoid need to accumulate +* Jacobi rotations in the Jacobi iterations. + DO 1965 p = 1, NR + CALL DCOPY( N-p+1, A(p,p), LDA, U(p,p), 1 ) + 1965 CONTINUE + CALL DLASET( 'Upper', NR-1, NR-1, ZERO, ZERO, U(1,2), LDU ) +* + CALL DGEQRF( N, NR, U, LDU, WORK(N+1), WORK(2*N+1), + $ LWORK-2*N, IERR ) +* + DO 1967 p = 1, NR - 1 + CALL DCOPY( NR-p, U(p,p+1), LDU, U(p+1,p), 1 ) + 1967 CONTINUE + CALL DLASET( 'Upper', NR-1, NR-1, ZERO, ZERO, U(1,2), LDU ) +* + CALL DGESVJ( 'Lower', 'U', 'N', NR,NR, U, LDU, SVA, NR, A, + $ LDA, WORK(N+1), LWORK-N, INFO ) + SCALEM = WORK(N+1) + NUMRANK = IDNINT(WORK(N+2)) +* + IF ( NR .LT. M ) THEN + CALL DLASET( 'A', M-NR, NR,ZERO, ZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL DLASET( 'A',NR, N1-NR, ZERO, ZERO, U(1,NR+1), LDU ) + CALL DLASET( 'A',M-NR,N1-NR,ZERO,ONE,U(NR+1,NR+1), LDU ) + END IF + END IF +* + CALL DORMQR( 'Left', 'No Tr', M, N1, N, A, LDA, WORK, U, + $ LDU, WORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL DLASWP( N1, U, LDU, 1, M-1, IWORK(2*N+1), -1 ) +* + DO 1974 p = 1, N1 + XSC = ONE / DNRM2( M, U(1,p), 1 ) + CALL DSCAL( M, XSC, U(1,p), 1 ) + 1974 CONTINUE +* + IF ( TRANSP ) THEN + CALL DLACPY( 'All', N, N, U, LDU, V, LDV ) + END IF +* + ELSE +* +* .. Full SVD .. +* + IF ( .NOT. JRACC ) THEN +* + IF ( .NOT. ALMORT ) THEN +* +* Second Preconditioning Step (QRF [with pivoting]) +* Note that the composition of TRANSPOSE, QRF and TRANSPOSE is +* equivalent to an LQF CALL. Since in many libraries the QRF +* seems to be better optimized than the LQF, we do explicit +* transpose and use the QRF. This is subject to changes in an +* optimized implementation of DGEJSV. +* + DO 1968 p = 1, NR + CALL DCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + 1968 CONTINUE +* +* .. the following two loops perturb small entries to avoid +* denormals in the second QR factorization, where they are +* as good as zeros. This is done to avoid painfully slow +* computation with denormals. The relative size of the perturbation +* is a parameter that can be changed by the implementer. +* This perturbation device will be obsolete on machines with +* properly implemented arithmetic. +* To switch it off, set L2PERT=.FALSE. To remove it from the +* code, remove the action under L2PERT=.TRUE., leave the ELSE part. +* The following two loops should be blocked and fused with the +* transposed copy above. +* + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL) + DO 2969 q = 1, NR + TEMP1 = XSC*DABS( V(q,q) ) + DO 2968 p = 1, N + IF ( ( p .GT. q ) .AND. ( DABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) + $ V(p,q) = DSIGN( TEMP1, V(p,q) ) + IF ( p .LT. q ) V(p,q) = - V(p,q) + 2968 CONTINUE + 2969 CONTINUE + ELSE + CALL DLASET( 'U', NR-1, NR-1, ZERO, ZERO, V(1,2), LDV ) + END IF +* +* Estimate the row scaled condition number of R1 +* (If R1 is rectangular, N > NR, then the condition number +* of the leading NR x NR submatrix is estimated.) +* + CALL DLACPY( 'L', NR, NR, V, LDV, WORK(2*N+1), NR ) + DO 3950 p = 1, NR + TEMP1 = DNRM2(NR-p+1,WORK(2*N+(p-1)*NR+p),1) + CALL DSCAL(NR-p+1,ONE/TEMP1,WORK(2*N+(p-1)*NR+p),1) + 3950 CONTINUE + CALL DPOCON('Lower',NR,WORK(2*N+1),NR,ONE,TEMP1, + $ WORK(2*N+NR*NR+1),IWORK(M+2*N+1),IERR) + CONDR1 = ONE / DSQRT(TEMP1) +* .. here need a second opinion on the condition number +* .. then assume worst case scenario +* R1 is OK for inverse <=> CONDR1 .LT. DBLE(N) +* more conservative <=> CONDR1 .LT. DSQRT(DBLE(N)) +* + COND_OK = DSQRT(DBLE(NR)) +*[TP] COND_OK is a tuning parameter. + + IF ( CONDR1 .LT. COND_OK ) THEN +* .. the second QRF without pivoting. Note: in an optimized +* implementation, this QRF should be implemented as the QRF +* of a lower triangular matrix. +* R1^t = Q2 * R2 + CALL DGEQRF( N, NR, V, LDV, WORK(N+1), WORK(2*N+1), + $ LWORK-2*N, IERR ) +* + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL)/EPSLN + DO 3959 p = 2, NR + DO 3958 q = 1, p - 1 + TEMP1 = XSC * MIN(DABS(V(p,p)),DABS(V(q,q))) + IF ( DABS(V(q,p)) .LE. TEMP1 ) + $ V(q,p) = DSIGN( TEMP1, V(q,p) ) + 3958 CONTINUE + 3959 CONTINUE + END IF +* + IF ( NR .NE. N ) + $ CALL DLACPY( 'A', N, NR, V, LDV, WORK(2*N+1), N ) +* .. save ... +* +* .. this transposed copy should be better than naive + DO 1969 p = 1, NR - 1 + CALL DCOPY( NR-p, V(p,p+1), LDV, V(p+1,p), 1 ) + 1969 CONTINUE +* + CONDR2 = CONDR1 +* + ELSE +* +* .. ill-conditioned case: second QRF with pivoting +* Note that windowed pivoting would be equally good +* numerically, and more run-time efficient. So, in +* an optimal implementation, the next call to DGEQP3 +* should be replaced with eg. CALL SGEQPX (ACM TOMS #782) +* with properly (carefully) chosen parameters. +* +* R1^t * P2 = Q2 * R2 + DO 3003 p = 1, NR + IWORK(N+p) = 0 + 3003 CONTINUE + CALL DGEQP3( N, NR, V, LDV, IWORK(N+1), WORK(N+1), + $ WORK(2*N+1), LWORK-2*N, IERR ) +** CALL DGEQRF( N, NR, V, LDV, WORK(N+1), WORK(2*N+1), +** $ LWORK-2*N, IERR ) + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL) + DO 3969 p = 2, NR + DO 3968 q = 1, p - 1 + TEMP1 = XSC * MIN(DABS(V(p,p)),DABS(V(q,q))) + IF ( DABS(V(q,p)) .LE. TEMP1 ) + $ V(q,p) = DSIGN( TEMP1, V(q,p) ) + 3968 CONTINUE + 3969 CONTINUE + END IF +* + CALL DLACPY( 'A', N, NR, V, LDV, WORK(2*N+1), N ) +* + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL) + DO 8970 p = 2, NR + DO 8971 q = 1, p - 1 + TEMP1 = XSC * MIN(DABS(V(p,p)),DABS(V(q,q))) + V(p,q) = - DSIGN( TEMP1, V(q,p) ) + 8971 CONTINUE + 8970 CONTINUE + ELSE + CALL DLASET( 'L',NR-1,NR-1,ZERO,ZERO,V(2,1),LDV ) + END IF +* Now, compute R2 = L3 * Q3, the LQ factorization. + CALL DGELQF( NR, NR, V, LDV, WORK(2*N+N*NR+1), + $ WORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, IERR ) +* .. and estimate the condition number + CALL DLACPY( 'L',NR,NR,V,LDV,WORK(2*N+N*NR+NR+1),NR ) + DO 4950 p = 1, NR + TEMP1 = DNRM2( p, WORK(2*N+N*NR+NR+p), NR ) + CALL DSCAL( p, ONE/TEMP1, WORK(2*N+N*NR+NR+p), NR ) + 4950 CONTINUE + CALL DPOCON( 'L',NR,WORK(2*N+N*NR+NR+1),NR,ONE,TEMP1, + $ WORK(2*N+N*NR+NR+NR*NR+1),IWORK(M+2*N+1),IERR ) + CONDR2 = ONE / DSQRT(TEMP1) +* + IF ( CONDR2 .GE. COND_OK ) THEN +* .. save the Householder vectors used for Q3 +* (this overwrites the copy of R2, as it will not be +* needed in this branch, but it does not overwritte the +* Huseholder vectors of Q2.). + CALL DLACPY( 'U', NR, NR, V, LDV, WORK(2*N+1), N ) +* .. and the rest of the information on Q3 is in +* WORK(2*N+N*NR+1:2*N+N*NR+N) + END IF +* + END IF +* + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL) + DO 4968 q = 2, NR + TEMP1 = XSC * V(q,q) + DO 4969 p = 1, q - 1 +* V(p,q) = - DSIGN( TEMP1, V(q,p) ) + V(p,q) = - DSIGN( TEMP1, V(p,q) ) + 4969 CONTINUE + 4968 CONTINUE + ELSE + CALL DLASET( 'U', NR-1,NR-1, ZERO,ZERO, V(1,2), LDV ) + END IF +* +* Second preconditioning finished; continue with Jacobi SVD +* The input matrix is lower trinagular. +* +* Recover the right singular vectors as solution of a well +* conditioned triangular matrix equation. +* + IF ( CONDR1 .LT. COND_OK ) THEN +* + CALL DGESVJ( 'L','U','N',NR,NR,V,LDV,SVA,NR,U, + $ LDU,WORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,INFO ) + SCALEM = WORK(2*N+N*NR+NR+1) + NUMRANK = IDNINT(WORK(2*N+N*NR+NR+2)) + DO 3970 p = 1, NR + CALL DCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL DSCAL( NR, SVA(p), V(1,p), 1 ) + 3970 CONTINUE + +* .. pick the right matrix equation and solve it +* + IF ( NR .EQ. N ) THEN +* :)) .. best case, R1 is inverted. The solution of this matrix +* equation is Q2*V2 = the product of the Jacobi rotations +* used in DGESVJ, premultiplied with the orthogonal matrix +* from the second QR factorization. + CALL DTRSM( 'L','U','N','N', NR,NR,ONE, A,LDA, V,LDV ) + ELSE +* .. R1 is well conditioned, but non-square. Transpose(R2) +* is inverted to get the product of the Jacobi rotations +* used in DGESVJ. The Q-factor from the second QR +* factorization is then built in explicitly. + CALL DTRSM('L','U','T','N',NR,NR,ONE,WORK(2*N+1), + $ N,V,LDV) + IF ( NR .LT. N ) THEN + CALL DLASET('A',N-NR,NR,ZERO,ZERO,V(NR+1,1),LDV) + CALL DLASET('A',NR,N-NR,ZERO,ZERO,V(1,NR+1),LDV) + CALL DLASET('A',N-NR,N-NR,ZERO,ONE,V(NR+1,NR+1),LDV) + END IF + CALL DORMQR('L','N',N,N,NR,WORK(2*N+1),N,WORK(N+1), + $ V,LDV,WORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR) + END IF +* + ELSE IF ( CONDR2 .LT. COND_OK ) THEN +* +* :) .. the input matrix A is very likely a relative of +* the Kahan matrix :) +* The matrix R2 is inverted. The solution of the matrix equation +* is Q3^T*V3 = the product of the Jacobi rotations (appplied to +* the lower triangular L3 from the LQ factorization of +* R2=L3*Q3), pre-multiplied with the transposed Q3. + CALL DGESVJ( 'L', 'U', 'N', NR, NR, V, LDV, SVA, NR, U, + $ LDU, WORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, INFO ) + SCALEM = WORK(2*N+N*NR+NR+1) + NUMRANK = IDNINT(WORK(2*N+N*NR+NR+2)) + DO 3870 p = 1, NR + CALL DCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL DSCAL( NR, SVA(p), U(1,p), 1 ) + 3870 CONTINUE + CALL DTRSM('L','U','N','N',NR,NR,ONE,WORK(2*N+1),N,U,LDU) +* .. apply the permutation from the second QR factorization + DO 873 q = 1, NR + DO 872 p = 1, NR + WORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 872 CONTINUE + DO 874 p = 1, NR + U(p,q) = WORK(2*N+N*NR+NR+p) + 874 CONTINUE + 873 CONTINUE + IF ( NR .LT. N ) THEN + CALL DLASET( 'A',N-NR,NR,ZERO,ZERO,V(NR+1,1),LDV ) + CALL DLASET( 'A',NR,N-NR,ZERO,ZERO,V(1,NR+1),LDV ) + CALL DLASET( 'A',N-NR,N-NR,ZERO,ONE,V(NR+1,NR+1),LDV ) + END IF + CALL DORMQR( 'L','N',N,N,NR,WORK(2*N+1),N,WORK(N+1), + $ V,LDV,WORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) + ELSE +* Last line of defense. +* #:( This is a rather pathological case: no scaled condition +* improvement after two pivoted QR factorizations. Other +* possibility is that the rank revealing QR factorization +* or the condition estimator has failed, or the COND_OK +* is set very close to ONE (which is unnecessary). Normally, +* this branch should never be executed, but in rare cases of +* failure of the RRQR or condition estimator, the last line of +* defense ensures that DGEJSV completes the task. +* Compute the full SVD of L3 using DGESVJ with explicit +* accumulation of Jacobi rotations. + CALL DGESVJ( 'L', 'U', 'V', NR, NR, V, LDV, SVA, NR, U, + $ LDU, WORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, INFO ) + SCALEM = WORK(2*N+N*NR+NR+1) + NUMRANK = IDNINT(WORK(2*N+N*NR+NR+2)) + IF ( NR .LT. N ) THEN + CALL DLASET( 'A',N-NR,NR,ZERO,ZERO,V(NR+1,1),LDV ) + CALL DLASET( 'A',NR,N-NR,ZERO,ZERO,V(1,NR+1),LDV ) + CALL DLASET( 'A',N-NR,N-NR,ZERO,ONE,V(NR+1,NR+1),LDV ) + END IF + CALL DORMQR( 'L','N',N,N,NR,WORK(2*N+1),N,WORK(N+1), + $ V,LDV,WORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* + CALL DORMLQ( 'L', 'T', NR, NR, NR, WORK(2*N+1), N, + $ WORK(2*N+N*NR+1), U, LDU, WORK(2*N+N*NR+NR+1), + $ LWORK-2*N-N*NR-NR, IERR ) + DO 773 q = 1, NR + DO 772 p = 1, NR + WORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 772 CONTINUE + DO 774 p = 1, NR + U(p,q) = WORK(2*N+N*NR+NR+p) + 774 CONTINUE + 773 CONTINUE +* + END IF +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = DSQRT(DBLE(N)) * EPSLN + DO 1972 q = 1, N + DO 972 p = 1, N + WORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 972 CONTINUE + DO 973 p = 1, N + V(p,q) = WORK(2*N+N*NR+NR+p) + 973 CONTINUE + XSC = ONE / DNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL DSCAL( N, XSC, V(1,q), 1 ) + 1972 CONTINUE +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). + IF ( NR .LT. M ) THEN + CALL DLASET( 'A', M-NR, NR, ZERO, ZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL DLASET('A',NR,N1-NR,ZERO,ZERO,U(1,NR+1),LDU) + CALL DLASET('A',M-NR,N1-NR,ZERO,ONE,U(NR+1,NR+1),LDU) + END IF + END IF +* +* The Q matrix from the first QRF is built into the left singular +* matrix U. This applies to all cases. +* + CALL DORMQR( 'Left', 'No_Tr', M, N1, N, A, LDA, WORK, U, + $ LDU, WORK(N+1), LWORK-N, IERR ) + +* The columns of U are normalized. The cost is O(M*N) flops. + TEMP1 = DSQRT(DBLE(M)) * EPSLN + DO 1973 p = 1, NR + XSC = ONE / DNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL DSCAL( M, XSC, U(1,p), 1 ) + 1973 CONTINUE +* +* If the initial QRF is computed with row pivoting, the left +* singular vectors must be adjusted. +* + IF ( ROWPIV ) + $ CALL DLASWP( N1, U, LDU, 1, M-1, IWORK(2*N+1), -1 ) +* + ELSE +* +* .. the initial matrix A has almost orthogonal columns and +* the second QRF is not needed +* + CALL DLACPY( 'Upper', N, N, A, LDA, WORK(N+1), N ) + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL) + DO 5970 p = 2, N + TEMP1 = XSC * WORK( N + (p-1)*N + p ) + DO 5971 q = 1, p - 1 + WORK(N+(q-1)*N+p)=-DSIGN(TEMP1,WORK(N+(p-1)*N+q)) + 5971 CONTINUE + 5970 CONTINUE + ELSE + CALL DLASET( 'Lower',N-1,N-1,ZERO,ZERO,WORK(N+2),N ) + END IF +* + CALL DGESVJ( 'Upper', 'U', 'N', N, N, WORK(N+1), N, SVA, + $ N, U, LDU, WORK(N+N*N+1), LWORK-N-N*N, INFO ) +* + SCALEM = WORK(N+N*N+1) + NUMRANK = IDNINT(WORK(N+N*N+2)) + DO 6970 p = 1, N + CALL DCOPY( N, WORK(N+(p-1)*N+1), 1, U(1,p), 1 ) + CALL DSCAL( N, SVA(p), WORK(N+(p-1)*N+1), 1 ) + 6970 CONTINUE +* + CALL DTRSM( 'Left', 'Upper', 'NoTrans', 'No UD', N, N, + $ ONE, A, LDA, WORK(N+1), N ) + DO 6972 p = 1, N + CALL DCOPY( N, WORK(N+p), N, V(IWORK(p),1), LDV ) + 6972 CONTINUE + TEMP1 = DSQRT(DBLE(N))*EPSLN + DO 6971 p = 1, N + XSC = ONE / DNRM2( N, V(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL DSCAL( N, XSC, V(1,p), 1 ) + 6971 CONTINUE +* +* Assemble the left singular vector matrix U (M x N). +* + IF ( N .LT. M ) THEN + CALL DLASET( 'A', M-N, N, ZERO, ZERO, U(N+1,1), LDU ) + IF ( N .LT. N1 ) THEN + CALL DLASET( 'A',N, N1-N, ZERO, ZERO, U(1,N+1),LDU ) + CALL DLASET( 'A',M-N,N1-N, ZERO, ONE,U(N+1,N+1),LDU ) + END IF + END IF + CALL DORMQR( 'Left', 'No Tr', M, N1, N, A, LDA, WORK, U, + $ LDU, WORK(N+1), LWORK-N, IERR ) + TEMP1 = DSQRT(DBLE(M))*EPSLN + DO 6973 p = 1, N1 + XSC = ONE / DNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL DSCAL( M, XSC, U(1,p), 1 ) + 6973 CONTINUE +* + IF ( ROWPIV ) + $ CALL DLASWP( N1, U, LDU, 1, M-1, IWORK(2*N+1), -1 ) +* + END IF +* +* end of the >> almost orthogonal case << in the full SVD +* + ELSE +* +* This branch deploys a preconditioned Jacobi SVD with explicitly +* accumulated rotations. It is included as optional, mainly for +* experimental purposes. It does perform well, and can also be used. +* In this implementation, this branch will be automatically activated +* if the condition number sigma_max(A) / sigma_min(A) is predicted +* to be greater than the overflow threshold. This is because the +* a posteriori computation of the singular vectors assumes robust +* implementation of BLAS and some LAPACK procedures, capable of working +* in presence of extreme values. Since that is not always the case, ... +* + DO 7968 p = 1, NR + CALL DCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + 7968 CONTINUE +* + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL/EPSLN) + DO 5969 q = 1, NR + TEMP1 = XSC*DABS( V(q,q) ) + DO 5968 p = 1, N + IF ( ( p .GT. q ) .AND. ( DABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) + $ V(p,q) = DSIGN( TEMP1, V(p,q) ) + IF ( p .LT. q ) V(p,q) = - V(p,q) + 5968 CONTINUE + 5969 CONTINUE + ELSE + CALL DLASET( 'U', NR-1, NR-1, ZERO, ZERO, V(1,2), LDV ) + END IF + + CALL DGEQRF( N, NR, V, LDV, WORK(N+1), WORK(2*N+1), + $ LWORK-2*N, IERR ) + CALL DLACPY( 'L', N, NR, V, LDV, WORK(2*N+1), N ) +* + DO 7969 p = 1, NR + CALL DCOPY( NR-p+1, V(p,p), LDV, U(p,p), 1 ) + 7969 CONTINUE + + IF ( L2PERT ) THEN + XSC = DSQRT(SMALL/EPSLN) + DO 9970 q = 2, NR + DO 9971 p = 1, q - 1 + TEMP1 = XSC * MIN(DABS(U(p,p)),DABS(U(q,q))) + U(p,q) = - DSIGN( TEMP1, U(q,p) ) + 9971 CONTINUE + 9970 CONTINUE + ELSE + CALL DLASET('U', NR-1, NR-1, ZERO, ZERO, U(1,2), LDU ) + END IF + + CALL DGESVJ( 'G', 'U', 'V', NR, NR, U, LDU, SVA, + $ N, V, LDV, WORK(2*N+N*NR+1), LWORK-2*N-N*NR, INFO ) + SCALEM = WORK(2*N+N*NR+1) + NUMRANK = IDNINT(WORK(2*N+N*NR+2)) + + IF ( NR .LT. N ) THEN + CALL DLASET( 'A',N-NR,NR,ZERO,ZERO,V(NR+1,1),LDV ) + CALL DLASET( 'A',NR,N-NR,ZERO,ZERO,V(1,NR+1),LDV ) + CALL DLASET( 'A',N-NR,N-NR,ZERO,ONE,V(NR+1,NR+1),LDV ) + END IF + + CALL DORMQR( 'L','N',N,N,NR,WORK(2*N+1),N,WORK(N+1), + $ V,LDV,WORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = DSQRT(DBLE(N)) * EPSLN + DO 7972 q = 1, N + DO 8972 p = 1, N + WORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 8972 CONTINUE + DO 8973 p = 1, N + V(p,q) = WORK(2*N+N*NR+NR+p) + 8973 CONTINUE + XSC = ONE / DNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL DSCAL( N, XSC, V(1,q), 1 ) + 7972 CONTINUE +* +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). +* + IF ( NR .LT. M ) THEN + CALL DLASET( 'A', M-NR, NR, ZERO, ZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL DLASET( 'A',NR, N1-NR, ZERO, ZERO, U(1,NR+1),LDU ) + CALL DLASET( 'A',M-NR,N1-NR, ZERO, ONE,U(NR+1,NR+1),LDU ) + END IF + END IF +* + CALL DORMQR( 'Left', 'No Tr', M, N1, N, A, LDA, WORK, U, + $ LDU, WORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL DLASWP( N1, U, LDU, 1, M-1, IWORK(2*N+1), -1 ) +* +* + END IF + IF ( TRANSP ) THEN +* .. swap U and V because the procedure worked on A^t + DO 6974 p = 1, N + CALL DSWAP( N, U(1,p), 1, V(1,p), 1 ) + 6974 CONTINUE + END IF +* + END IF +* end of the full SVD +* +* Undo scaling, if necessary (and possible) +* + IF ( USCAL2 .LE. (BIG/SVA(1))*USCAL1 ) THEN + CALL DLASCL( 'G', 0, 0, USCAL1, USCAL2, NR, 1, SVA, N, IERR ) + USCAL1 = ONE + USCAL2 = ONE + END IF +* + IF ( NR .LT. N ) THEN + DO 3004 p = NR+1, N + SVA(p) = ZERO + 3004 CONTINUE + END IF +* + WORK(1) = USCAL2 * SCALEM + WORK(2) = USCAL1 + IF ( ERREST ) WORK(3) = SCONDA + IF ( LSVEC .AND. RSVEC ) THEN + WORK(4) = CONDR1 + WORK(5) = CONDR2 + END IF + IF ( L2TRAN ) THEN + WORK(6) = ENTRA + WORK(7) = ENTRAT + END IF +* + IWORK(1) = NR + IWORK(2) = NUMRANK + IWORK(3) = WARNING +* + RETURN +* .. +* .. END OF DGEJSV +* .. + END +* diff --git a/lapack-netlib/dgesvx.f b/lapack-netlib/dgesvx.f new file mode 100644 index 000000000..f787488dc --- /dev/null +++ b/lapack-netlib/dgesvx.f @@ -0,0 +1,599 @@ +*> \brief DGESVX computes the solution to system of linear equations A * X = B for GE matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download DGESVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE DGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, +* EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, +* WORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS +* DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ), IWORK( * ) +* DOUBLE PRECISION A( LDA, * ), AF( LDAF, * ), B( LDB, * ), +* $ BERR( * ), C( * ), FERR( * ), R( * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> DGESVX uses the LU factorization to compute the solution to a real +*> system of linear equations +*> A * X = B, +*> where A is an N-by-N matrix and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = P * L * U, +*> where P is a permutation matrix, L is a unit lower triangular +*> matrix, and U is upper triangular. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AF and IPIV contain the factored form of A. +*> If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> A, AF, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AF and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AF and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations: +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is DOUBLE PRECISION array, dimension (LDA,N) +*> On entry, the N-by-N matrix A. If FACT = 'F' and EQUED is +*> not 'N', then A must have been equilibrated by the scaling +*> factors in R and/or C. A is not modified if FACT = 'F' or +*> 'N', or if FACT = 'E' and EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] AF +*> \verbatim +*> AF is DOUBLE PRECISION array, dimension (LDAF,N) +*> If FACT = 'F', then AF is an input argument and on entry +*> contains the factors L and U from the factorization +*> A = P*L*U as computed by DGETRF. If EQUED .ne. 'N', then +*> AF is the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the equilibrated matrix A (see the description of A for +*> the form of the equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAF +*> \verbatim +*> LDAF is INTEGER +*> The leading dimension of the array AF. LDAF >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = P*L*U +*> as computed by DGETRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is DOUBLE PRECISION array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is DOUBLE PRECISION array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is DOUBLE PRECISION array, dimension (LDB,NRHS) +*> On entry, the N-by-NRHS right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is DOUBLE PRECISION array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is DOUBLE PRECISION +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is DOUBLE PRECISION array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is DOUBLE PRECISION array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is DOUBLE PRECISION array, dimension (MAX(1,4*N)) +*> On exit, WORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If WORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 WORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, dimension (N) +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization has +*> been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup doubleGEsolve +* +* ===================================================================== + SUBROUTINE DGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, + $ EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, + $ WORK, IWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS + DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ), IWORK( * ) + DOUBLE PRECISION A( LDA, * ), AF( LDAF, * ), B( LDB, * ), + $ BERR( * ), C( * ), FERR( * ), R( * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* +* .. Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J + DOUBLE PRECISION AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + DOUBLE PRECISION DLAMCH, DLANGE, DLANTR + EXTERNAL LSAME, DLAMCH, DLANGE, DLANTR +* .. +* .. External Subroutines .. + EXTERNAL DGECON, DGEEQU, DGERFS, DGETRF, DGETRS, DLACPY, + $ DLAQGE, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = DLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -4 + ELSE IF( LDA.LT.MAX( 1, N ) ) THEN + INFO = -6 + ELSE IF( LDAF.LT.MAX( 1, N ) ) THEN + INFO = -8 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -10 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -11 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -12 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -14 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -16 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'DGESVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL DGEEQU( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL DLAQGE( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, + $ EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of A. +* + CALL DLACPY( 'Full', N, N, A, LDA, AF, LDAF ) + CALL DGETRF( N, N, AF, LDAF, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + RPVGRW = DLANTR( 'M', 'U', 'N', INFO, INFO, AF, LDAF, + $ WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = DLANGE( 'M', N, INFO, A, LDA, WORK ) / RPVGRW + END IF + WORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = DLANGE( NORM, N, N, A, LDA, WORK ) + RPVGRW = DLANTR( 'M', 'U', 'N', N, N, AF, LDAF, WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = DLANGE( 'M', N, N, A, LDA, WORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL DGECON( NORM, N, AF, LDAF, ANORM, RCOND, WORK, IWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL DLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL DGETRS( TRANS, N, NRHS, AF, LDAF, IPIV, X, LDX, INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL DGERFS( TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, B, LDB, X, + $ LDX, FERR, BERR, WORK, IWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 80 J = 1, NRHS + DO 70 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 70 CONTINUE + 80 CONTINUE + DO 90 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 90 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 120 CONTINUE + END IF +* + WORK( 1 ) = RPVGRW +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.DLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 + RETURN +* +* End of DGESVX +* + END diff --git a/lapack-netlib/sgbsvx.f b/lapack-netlib/sgbsvx.f new file mode 100644 index 000000000..df3a721d9 --- /dev/null +++ b/lapack-netlib/sgbsvx.f @@ -0,0 +1,641 @@ +*> \brief SGBSVX computes the solution to system of linear equations A * X = B for GB matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download SGBSVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE SGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, +* LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, +* RCOND, FERR, BERR, WORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS +* REAL RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ), IWORK( * ) +* REAL AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), +* $ BERR( * ), C( * ), FERR( * ), R( * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> SGBSVX uses the LU factorization to compute the solution to a real +*> system of linear equations A * X = B, A**T * X = B, or A**H * X = B, +*> where A is a band matrix of order N with KL subdiagonals and KU +*> superdiagonals, and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed by this subroutine: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = L * U, +*> where L is a product of permutation and unit lower triangular +*> matrices with KL subdiagonals, and U is upper triangular with +*> KL+KU superdiagonals. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AFB and IPIV contain the factored form of +*> A. If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> AB, AFB, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AFB and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AFB and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations. +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] KL +*> \verbatim +*> KL is INTEGER +*> The number of subdiagonals within the band of A. KL >= 0. +*> \endverbatim +*> +*> \param[in] KU +*> \verbatim +*> KU is INTEGER +*> The number of superdiagonals within the band of A. KU >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] AB +*> \verbatim +*> AB is REAL array, dimension (LDAB,N) +*> On entry, the matrix A in band storage, in rows 1 to KL+KU+1. +*> The j-th column of A is stored in the j-th column of the +*> array AB as follows: +*> AB(KU+1+i-j,j) = A(i,j) for max(1,j-KU)<=i<=min(N,j+kl) +*> +*> If FACT = 'F' and EQUED is not 'N', then A must have been +*> equilibrated by the scaling factors in R and/or C. AB is not +*> modified if FACT = 'F' or 'N', or if FACT = 'E' and +*> EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDAB +*> \verbatim +*> LDAB is INTEGER +*> The leading dimension of the array AB. LDAB >= KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] AFB +*> \verbatim +*> AFB is REAL array, dimension (LDAFB,N) +*> If FACT = 'F', then AFB is an input argument and on entry +*> contains details of the LU factorization of the band matrix +*> A, as computed by SGBTRF. U is stored as an upper triangular +*> band matrix with KL+KU superdiagonals in rows 1 to KL+KU+1, +*> and the multipliers used during the factorization are stored +*> in rows KL+KU+2 to 2*KL+KU+1. If EQUED .ne. 'N', then AFB is +*> the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AFB is an output argument and on exit +*> returns details of the LU factorization of A. +*> +*> If FACT = 'E', then AFB is an output argument and on exit +*> returns details of the LU factorization of the equilibrated +*> matrix A (see the description of AB for the form of the +*> equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAFB +*> \verbatim +*> LDAFB is INTEGER +*> The leading dimension of the array AFB. LDAFB >= 2*KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = L*U +*> as computed by SGBTRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is REAL array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is REAL array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is REAL array, dimension (LDB,NRHS) +*> On entry, the right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is REAL array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is REAL +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is REAL array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is REAL array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is REAL array, dimension (MAX(1,3*N)) +*> On exit, WORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If WORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 WORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, dimension (N) +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization +*> has been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup realGBsolve +* +* ===================================================================== + SUBROUTINE SGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, + $ LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, + $ RCOND, FERR, BERR, WORK, IWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS + REAL RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ), IWORK( * ) + REAL AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), + $ BERR( * ), C( * ), FERR( * ), R( * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* Moved setting of INFO = N+1 so INFO does not subsequently get +* overwritten. Sven, 17 Mar 05. +* ===================================================================== +* +* .. Parameters .. + REAL ZERO, ONE + PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J, J1, J2 + REAL AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + REAL SLAMCH, SLANGB, SLANTB + EXTERNAL LSAME, SLAMCH, SLANGB, SLANTB +* .. +* .. External Subroutines .. + EXTERNAL SCOPY, SGBCON, SGBEQU, SGBRFS, SGBTRF, SGBTRS, + $ SLACPY, SLAQGB, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC ABS, MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = SLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( KL.LT.0 ) THEN + INFO = -4 + ELSE IF( KU.LT.0 ) THEN + INFO = -5 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -6 + ELSE IF( LDAB.LT.KL+KU+1 ) THEN + INFO = -8 + ELSE IF( LDAFB.LT.2*KL+KU+1 ) THEN + INFO = -10 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -12 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -13 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -14 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -16 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -18 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'SGBSVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL SGBEQU( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL SLAQGB( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of the band matrix A. +* + DO 70 J = 1, N + J1 = MAX( J-KU, 1 ) + J2 = MIN( J+KL, N ) + CALL SCOPY( J2-J1+1, AB( KU+1-J+J1, J ), 1, + $ AFB( KL+KU+1-J+J1, J ), 1 ) + 70 CONTINUE +* + CALL SGBTRF( N, N, KL, KU, AFB, LDAFB, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + ANORM = ZERO + DO 90 J = 1, INFO + DO 80 I = MAX( KU+2-J, 1 ), MIN( N+KU+1-J, KL+KU+1 ) + ANORM = MAX( ANORM, ABS( AB( I, J ) ) ) + 80 CONTINUE + 90 CONTINUE + RPVGRW = SLANTB( 'M', 'U', 'N', INFO, MIN( INFO-1, KL+KU ), + $ AFB( MAX( 1, KL+KU+2-INFO ), 1 ), LDAFB, + $ WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ANORM / RPVGRW + END IF + WORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = SLANGB( NORM, N, KL, KU, AB, LDAB, WORK ) + RPVGRW = SLANTB( 'M', 'U', 'N', N, KL+KU, AFB, LDAFB, WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = SLANGB( 'M', N, KL, KU, AB, LDAB, WORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL SGBCON( NORM, N, KL, KU, AFB, LDAFB, IPIV, ANORM, RCOND, + $ WORK, IWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL SLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL SGBTRS( TRANS, N, KL, KU, NRHS, AFB, LDAFB, IPIV, X, LDX, + $ INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL SGBRFS( TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, LDAFB, IPIV, + $ B, LDB, X, LDX, FERR, BERR, WORK, IWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 120 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 140 J = 1, NRHS + DO 130 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 130 CONTINUE + 140 CONTINUE + DO 150 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 150 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.SLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + WORK( 1 ) = RPVGRW + RETURN +* +* End of SGBSVX +* + END diff --git a/lapack-netlib/sgesvx.f b/lapack-netlib/sgesvx.f new file mode 100644 index 000000000..385e626cf --- /dev/null +++ b/lapack-netlib/sgesvx.f @@ -0,0 +1,599 @@ +*> \brief SGESVX computes the solution to system of linear equations A * X = B for GE matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download SGESVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE SGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, +* EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, +* WORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS +* REAL RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ), IWORK( * ) +* REAL A( LDA, * ), AF( LDAF, * ), B( LDB, * ), +* $ BERR( * ), C( * ), FERR( * ), R( * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> SGESVX uses the LU factorization to compute the solution to a real +*> system of linear equations +*> A * X = B, +*> where A is an N-by-N matrix and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = P * L * U, +*> where P is a permutation matrix, L is a unit lower triangular +*> matrix, and U is upper triangular. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AF and IPIV contain the factored form of A. +*> If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> A, AF, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AF and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AF and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations: +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is REAL array, dimension (LDA,N) +*> On entry, the N-by-N matrix A. If FACT = 'F' and EQUED is +*> not 'N', then A must have been equilibrated by the scaling +*> factors in R and/or C. A is not modified if FACT = 'F' or +*> 'N', or if FACT = 'E' and EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] AF +*> \verbatim +*> AF is REAL array, dimension (LDAF,N) +*> If FACT = 'F', then AF is an input argument and on entry +*> contains the factors L and U from the factorization +*> A = P*L*U as computed by SGETRF. If EQUED .ne. 'N', then +*> AF is the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the equilibrated matrix A (see the description of A for +*> the form of the equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAF +*> \verbatim +*> LDAF is INTEGER +*> The leading dimension of the array AF. LDAF >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = P*L*U +*> as computed by SGETRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is REAL array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is REAL array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is REAL array, dimension (LDB,NRHS) +*> On entry, the N-by-NRHS right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is REAL array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is REAL +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is REAL array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is REAL array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is REAL array, dimension (MAX(1,4*N)) +*> On exit, WORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If WORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 WORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, dimension (N) +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization has +*> been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup realGEsolve +* +* ===================================================================== + SUBROUTINE SGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, + $ EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, + $ WORK, IWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS + REAL RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ), IWORK( * ) + REAL A( LDA, * ), AF( LDAF, * ), B( LDB, * ), + $ BERR( * ), C( * ), FERR( * ), R( * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* +* .. Parameters .. + REAL ZERO, ONE + PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J + REAL AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + REAL SLAMCH, SLANGE, SLANTR + EXTERNAL LSAME, SLAMCH, SLANGE, SLANTR +* .. +* .. External Subroutines .. + EXTERNAL SGECON, SGEEQU, SGERFS, SGETRF, SGETRS, SLACPY, + $ SLAQGE, XERBLA +* .. +* .. Intrinsic Functions .. + INTRINSIC MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = SLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -4 + ELSE IF( LDA.LT.MAX( 1, N ) ) THEN + INFO = -6 + ELSE IF( LDAF.LT.MAX( 1, N ) ) THEN + INFO = -8 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -10 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -11 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -12 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -14 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -16 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'SGESVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL SGEEQU( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL SLAQGE( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, + $ EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of A. +* + CALL SLACPY( 'Full', N, N, A, LDA, AF, LDAF ) + CALL SGETRF( N, N, AF, LDAF, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + RPVGRW = SLANTR( 'M', 'U', 'N', INFO, INFO, AF, LDAF, + $ WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = SLANGE( 'M', N, INFO, A, LDA, WORK ) / RPVGRW + END IF + WORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = SLANGE( NORM, N, N, A, LDA, WORK ) + RPVGRW = SLANTR( 'M', 'U', 'N', N, N, AF, LDAF, WORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = SLANGE( 'M', N, N, A, LDA, WORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL SGECON( NORM, N, AF, LDAF, ANORM, RCOND, WORK, IWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL SLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL SGETRS( TRANS, N, NRHS, AF, LDAF, IPIV, X, LDX, INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL SGERFS( TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, B, LDB, X, + $ LDX, FERR, BERR, WORK, IWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 80 J = 1, NRHS + DO 70 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 70 CONTINUE + 80 CONTINUE + DO 90 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 90 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 120 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.SLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + WORK( 1 ) = RPVGRW + RETURN +* +* End of SGESVX +* + END diff --git a/lapack-netlib/zgbsvx.f b/lapack-netlib/zgbsvx.f new file mode 100644 index 000000000..871564a81 --- /dev/null +++ b/lapack-netlib/zgbsvx.f @@ -0,0 +1,644 @@ +*> \brief ZGBSVX computes the solution to system of linear equations A * X = B for GB matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download ZGBSVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE ZGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, +* LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, +* RCOND, FERR, BERR, WORK, RWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS +* DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ) +* DOUBLE PRECISION BERR( * ), C( * ), FERR( * ), R( * ), +* $ RWORK( * ) +* COMPLEX*16 AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> ZGBSVX uses the LU factorization to compute the solution to a complex +*> system of linear equations A * X = B, A**T * X = B, or A**H * X = B, +*> where A is a band matrix of order N with KL subdiagonals and KU +*> superdiagonals, and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed by this subroutine: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = L * U, +*> where L is a product of permutation and unit lower triangular +*> matrices with KL subdiagonals, and U is upper triangular with +*> KL+KU superdiagonals. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AFB and IPIV contain the factored form of +*> A. If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> AB, AFB, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AFB and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AFB and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations. +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Conjugate transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] KL +*> \verbatim +*> KL is INTEGER +*> The number of subdiagonals within the band of A. KL >= 0. +*> \endverbatim +*> +*> \param[in] KU +*> \verbatim +*> KU is INTEGER +*> The number of superdiagonals within the band of A. KU >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] AB +*> \verbatim +*> AB is COMPLEX*16 array, dimension (LDAB,N) +*> On entry, the matrix A in band storage, in rows 1 to KL+KU+1. +*> The j-th column of A is stored in the j-th column of the +*> array AB as follows: +*> AB(KU+1+i-j,j) = A(i,j) for max(1,j-KU)<=i<=min(N,j+kl) +*> +*> If FACT = 'F' and EQUED is not 'N', then A must have been +*> equilibrated by the scaling factors in R and/or C. AB is not +*> modified if FACT = 'F' or 'N', or if FACT = 'E' and +*> EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDAB +*> \verbatim +*> LDAB is INTEGER +*> The leading dimension of the array AB. LDAB >= KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] AFB +*> \verbatim +*> AFB is COMPLEX*16 array, dimension (LDAFB,N) +*> If FACT = 'F', then AFB is an input argument and on entry +*> contains details of the LU factorization of the band matrix +*> A, as computed by ZGBTRF. U is stored as an upper triangular +*> band matrix with KL+KU superdiagonals in rows 1 to KL+KU+1, +*> and the multipliers used during the factorization are stored +*> in rows KL+KU+2 to 2*KL+KU+1. If EQUED .ne. 'N', then AFB is +*> the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AFB is an output argument and on exit +*> returns details of the LU factorization of A. +*> +*> If FACT = 'E', then AFB is an output argument and on exit +*> returns details of the LU factorization of the equilibrated +*> matrix A (see the description of AB for the form of the +*> equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAFB +*> \verbatim +*> LDAFB is INTEGER +*> The leading dimension of the array AFB. LDAFB >= 2*KL+KU+1. +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = L*U +*> as computed by ZGBTRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is DOUBLE PRECISION array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is DOUBLE PRECISION array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is COMPLEX*16 array, dimension (LDB,NRHS) +*> On entry, the right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is COMPLEX*16 array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is DOUBLE PRECISION +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is DOUBLE PRECISION array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is DOUBLE PRECISION array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is COMPLEX*16 array, dimension (2*N) +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is DOUBLE PRECISION array, dimension (MAX(1,N)) +*> On exit, RWORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If RWORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 RWORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization +*> has been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complex16GBsolve +* +* ===================================================================== + SUBROUTINE ZGBSVX( FACT, TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, + $ LDAFB, IPIV, EQUED, R, C, B, LDB, X, LDX, + $ RCOND, FERR, BERR, WORK, RWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, KL, KU, LDAB, LDAFB, LDB, LDX, N, NRHS + DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ) + DOUBLE PRECISION BERR( * ), C( * ), FERR( * ), R( * ), + $ RWORK( * ) + COMPLEX*16 AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* Moved setting of INFO = N+1 so INFO does not subsequently get +* overwritten. Sven, 17 Mar 05. +* ===================================================================== +* +* .. Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J, J1, J2 + DOUBLE PRECISION AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + DOUBLE PRECISION DLAMCH, ZLANGB, ZLANTB + EXTERNAL LSAME, DLAMCH, ZLANGB, ZLANTB +* .. +* .. External Subroutines .. + EXTERNAL XERBLA, ZCOPY, ZGBCON, ZGBEQU, ZGBRFS, ZGBTRF, + $ ZGBTRS, ZLACPY, ZLAQGB +* .. +* .. Intrinsic Functions .. + INTRINSIC ABS, MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = DLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( KL.LT.0 ) THEN + INFO = -4 + ELSE IF( KU.LT.0 ) THEN + INFO = -5 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -6 + ELSE IF( LDAB.LT.KL+KU+1 ) THEN + INFO = -8 + ELSE IF( LDAFB.LT.2*KL+KU+1 ) THEN + INFO = -10 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -12 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -13 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -14 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -16 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -18 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'ZGBSVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL ZGBEQU( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL ZLAQGB( N, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, + $ AMAX, EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of the band matrix A. +* + DO 70 J = 1, N + J1 = MAX( J-KU, 1 ) + J2 = MIN( J+KL, N ) + CALL ZCOPY( J2-J1+1, AB( KU+1-J+J1, J ), 1, + $ AFB( KL+KU+1-J+J1, J ), 1 ) + 70 CONTINUE +* + CALL ZGBTRF( N, N, KL, KU, AFB, LDAFB, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + ANORM = ZERO + DO 90 J = 1, INFO + DO 80 I = MAX( KU+2-J, 1 ), MIN( N+KU+1-J, KL+KU+1 ) + ANORM = MAX( ANORM, ABS( AB( I, J ) ) ) + 80 CONTINUE + 90 CONTINUE + RPVGRW = ZLANTB( 'M', 'U', 'N', INFO, MIN( INFO-1, KL+KU ), + $ AFB( MAX( 1, KL+KU+2-INFO ), 1 ), LDAFB, + $ RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ANORM / RPVGRW + END IF + RWORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = ZLANGB( NORM, N, KL, KU, AB, LDAB, RWORK ) + RPVGRW = ZLANTB( 'M', 'U', 'N', N, KL+KU, AFB, LDAFB, RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ZLANGB( 'M', N, KL, KU, AB, LDAB, RWORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL ZGBCON( NORM, N, KL, KU, AFB, LDAFB, IPIV, ANORM, RCOND, + $ WORK, RWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL ZLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL ZGBTRS( TRANS, N, KL, KU, NRHS, AFB, LDAFB, IPIV, X, LDX, + $ INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL ZGBRFS( TRANS, N, KL, KU, NRHS, AB, LDAB, AFB, LDAFB, IPIV, + $ B, LDB, X, LDX, FERR, BERR, WORK, RWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 120 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 140 J = 1, NRHS + DO 130 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 130 CONTINUE + 140 CONTINUE + DO 150 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 150 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.DLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + RWORK( 1 ) = RPVGRW + RETURN +* +* End of ZGBSVX +* + END diff --git a/lapack-netlib/zgejsv.f b/lapack-netlib/zgejsv.f new file mode 100644 index 000000000..5fe899e50 --- /dev/null +++ b/lapack-netlib/zgejsv.f @@ -0,0 +1,2234 @@ +*> \brief \b ZGEJSV +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download ZGEJSV + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE ZGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, +* M, N, A, LDA, SVA, U, LDU, V, LDV, +* CWORK, LWORK, RWORK, LRWORK, IWORK, INFO ) +* +* .. Scalar Arguments .. +* IMPLICIT NONE +* INTEGER INFO, LDA, LDU, LDV, LWORK, M, N +* .. +* .. Array Arguments .. +* COMPLEX*16 A( LDA, * ), U( LDU, * ), V( LDV, * ), CWORK( LWORK ) +* DOUBLE PRECISION SVA( N ), RWORK( LRWORK ) +* INTEGER IWORK( * ) +* CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> ZGEJSV computes the singular value decomposition (SVD) of a complex M-by-N +*> matrix [A], where M >= N. The SVD of [A] is written as +*> +*> [A] = [U] * [SIGMA] * [V]^*, +*> +*> where [SIGMA] is an N-by-N (M-by-N) matrix which is zero except for its N +*> diagonal elements, [U] is an M-by-N (or M-by-M) unitary matrix, and +*> [V] is an N-by-N unitary matrix. The diagonal elements of [SIGMA] are +*> the singular values of [A]. The columns of [U] and [V] are the left and +*> the right singular vectors of [A], respectively. The matrices [U] and [V] +*> are computed and stored in the arrays U and V, respectively. The diagonal +*> of [SIGMA] is computed and stored in the array SVA. +*> \endverbatim +*> +*> Arguments: +*> ========== +*> +*> \param[in] JOBA +*> \verbatim +*> JOBA is CHARACTER*1 +*> Specifies the level of accuracy: +*> = 'C': This option works well (high relative accuracy) if A = B * D, +*> with well-conditioned B and arbitrary diagonal matrix D. +*> The accuracy cannot be spoiled by COLUMN scaling. The +*> accuracy of the computed output depends on the condition of +*> B, and the procedure aims at the best theoretical accuracy. +*> The relative error max_{i=1:N}|d sigma_i| / sigma_i is +*> bounded by f(M,N)*epsilon* cond(B), independent of D. +*> The input matrix is preprocessed with the QRF with column +*> pivoting. This initial preprocessing and preconditioning by +*> a rank revealing QR factorization is common for all values of +*> JOBA. Additional actions are specified as follows: +*> = 'E': Computation as with 'C' with an additional estimate of the +*> condition number of B. It provides a realistic error bound. +*> = 'F': If A = D1 * C * D2 with ill-conditioned diagonal scalings +*> D1, D2, and well-conditioned matrix C, this option gives +*> higher accuracy than the 'C' option. If the structure of the +*> input matrix is not known, and relative accuracy is +*> desirable, then this option is advisable. The input matrix A +*> is preprocessed with QR factorization with FULL (row and +*> column) pivoting. +*> = 'G': Computation as with 'F' with an additional estimate of the +*> condition number of B, where A=B*D. If A has heavily weighted +*> rows, then using this condition number gives too pessimistic +*> error bound. +*> = 'A': Small singular values are not well determined by the data +*> and are considered as noisy; the matrix is treated as +*> numerically rank deficient. The error in the computed +*> singular values is bounded by f(m,n)*epsilon*||A||. +*> The computed SVD A = U * S * V^* restores A up to +*> f(m,n)*epsilon*||A||. +*> This gives the procedure the licence to discard (set to zero) +*> all singular values below N*epsilon*||A||. +*> = 'R': Similar as in 'A'. Rank revealing property of the initial +*> QR factorization is used do reveal (using triangular factor) +*> a gap sigma_{r+1} < epsilon * sigma_r in which case the +*> numerical RANK is declared to be r. The SVD is computed with +*> absolute error bounds, but more accurately than with 'A'. +*> \endverbatim +*> +*> \param[in] JOBU +*> \verbatim +*> JOBU is CHARACTER*1 +*> Specifies whether to compute the columns of U: +*> = 'U': N columns of U are returned in the array U. +*> = 'F': full set of M left sing. vectors is returned in the array U. +*> = 'W': U may be used as workspace of length M*N. See the description +*> of U. +*> = 'N': U is not computed. +*> \endverbatim +*> +*> \param[in] JOBV +*> \verbatim +*> JOBV is CHARACTER*1 +*> Specifies whether to compute the matrix V: +*> = 'V': N columns of V are returned in the array V; Jacobi rotations +*> are not explicitly accumulated. +*> = 'J': N columns of V are returned in the array V, but they are +*> computed as the product of Jacobi rotations, if JOBT = 'N'. +*> = 'W': V may be used as workspace of length N*N. See the description +*> of V. +*> = 'N': V is not computed. +*> \endverbatim +*> +*> \param[in] JOBR +*> \verbatim +*> JOBR is CHARACTER*1 +*> Specifies the RANGE for the singular values. Issues the licence to +*> set to zero small positive singular values if they are outside +*> specified range. If A .NE. 0 is scaled so that the largest singular +*> value of c*A is around SQRT(BIG), BIG=DLAMCH('O'), then JOBR issues +*> the licence to kill columns of A whose norm in c*A is less than +*> SQRT(SFMIN) (for JOBR = 'R'), or less than SMALL=SFMIN/EPSLN, +*> where SFMIN=DLAMCH('S'), EPSLN=DLAMCH('E'). +*> = 'N': Do not kill small columns of c*A. This option assumes that +*> BLAS and QR factorizations and triangular solvers are +*> implemented to work in that range. If the condition of A +*> is greater than BIG, use ZGESVJ. +*> = 'R': RESTRICTED range for sigma(c*A) is [SQRT(SFMIN), SQRT(BIG)] +*> (roughly, as described above). This option is recommended. +*> =========================== +*> For computing the singular values in the FULL range [SFMIN,BIG] +*> use ZGESVJ. +*> \endverbatim +*> +*> \param[in] JOBT +*> \verbatim +*> JOBT is CHARACTER*1 +*> If the matrix is square then the procedure may determine to use +*> transposed A if A^* seems to be better with respect to convergence. +*> If the matrix is not square, JOBT is ignored. +*> The decision is based on two values of entropy over the adjoint +*> orbit of A^* * A. See the descriptions of RWORK(6) and RWORK(7). +*> = 'T': transpose if entropy test indicates possibly faster +*> convergence of Jacobi process if A^* is taken as input. If A is +*> replaced with A^*, then the row pivoting is included automatically. +*> = 'N': do not speculate. +*> The option 'T' can be used to compute only the singular values, or +*> the full SVD (U, SIGMA and V). For only one set of singular vectors +*> (U or V), the caller should provide both U and V, as one of the +*> matrices is used as workspace if the matrix A is transposed. +*> The implementer can easily remove this constraint and make the +*> code more complicated. See the descriptions of U and V. +*> In general, this option is considered experimental, and 'N'; should +*> be preferred. This is subject to changes in the future. +*> \endverbatim +*> +*> \param[in] JOBP +*> \verbatim +*> JOBP is CHARACTER*1 +*> Issues the licence to introduce structured perturbations to drown +*> denormalized numbers. This licence should be active if the +*> denormals are poorly implemented, causing slow computation, +*> especially in cases of fast convergence (!). For details see [1,2]. +*> For the sake of simplicity, this perturbations are included only +*> when the full SVD or only the singular values are requested. The +*> implementer/user can easily add the perturbation for the cases of +*> computing one set of singular vectors. +*> = 'P': introduce perturbation +*> = 'N': do not perturb +*> \endverbatim +*> +*> \param[in] M +*> \verbatim +*> M is INTEGER +*> The number of rows of the input matrix A. M >= 0. +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of columns of the input matrix A. M >= N >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is COMPLEX*16 array, dimension (LDA,N) +*> On entry, the M-by-N matrix A. +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,M). +*> \endverbatim +*> +*> \param[out] SVA +*> \verbatim +*> SVA is DOUBLE PRECISION array, dimension (N) +*> On exit, +*> - For RWORK(1)/RWORK(2) = ONE: The singular values of A. During +*> the computation SVA contains Euclidean column norms of the +*> iterated matrices in the array A. +*> - For RWORK(1) .NE. RWORK(2): The singular values of A are +*> (RWORK(1)/RWORK(2)) * SVA(1:N). This factored form is used if +*> sigma_max(A) overflows or if small singular values have been +*> saved from underflow by scaling the input matrix A. +*> - If JOBR='R' then some of the singular values may be returned +*> as exact zeros obtained by "set to zero" because they are +*> below the numerical rank threshold or are denormalized numbers. +*> \endverbatim +*> +*> \param[out] U +*> \verbatim +*> U is COMPLEX*16 array, dimension ( LDU, N ) +*> If JOBU = 'U', then U contains on exit the M-by-N matrix of +*> the left singular vectors. +*> If JOBU = 'F', then U contains on exit the M-by-M matrix of +*> the left singular vectors, including an ONB +*> of the orthogonal complement of the Range(A). +*> If JOBU = 'W' .AND. (JOBV = 'V' .AND. JOBT = 'T' .AND. M = N), +*> then U is used as workspace if the procedure +*> replaces A with A^*. In that case, [V] is computed +*> in U as left singular vectors of A^* and then +*> copied back to the V array. This 'W' option is just +*> a reminder to the caller that in this case U is +*> reserved as workspace of length N*N. +*> If JOBU = 'N' U is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDU +*> \verbatim +*> LDU is INTEGER +*> The leading dimension of the array U, LDU >= 1. +*> IF JOBU = 'U' or 'F' or 'W', then LDU >= M. +*> \endverbatim +*> +*> \param[out] V +*> \verbatim +*> V is COMPLEX*16 array, dimension ( LDV, N ) +*> If JOBV = 'V', 'J' then V contains on exit the N-by-N matrix of +*> the right singular vectors; +*> If JOBV = 'W', AND (JOBU = 'U' AND JOBT = 'T' AND M = N), +*> then V is used as workspace if the pprocedure +*> replaces A with A^*. In that case, [U] is computed +*> in V as right singular vectors of A^* and then +*> copied back to the U array. This 'W' option is just +*> a reminder to the caller that in this case V is +*> reserved as workspace of length N*N. +*> If JOBV = 'N' V is not referenced, unless JOBT='T'. +*> \endverbatim +*> +*> \param[in] LDV +*> \verbatim +*> LDV is INTEGER +*> The leading dimension of the array V, LDV >= 1. +*> If JOBV = 'V' or 'J' or 'W', then LDV >= N. +*> \endverbatim +*> +*> \param[out] CWORK +*> \verbatim +*> CWORK is COMPLEX*16 array, dimension (MAX(2,LWORK)) +*> If the call to ZGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit CWORK(1) contains the required length of +*> CWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[in] LWORK +*> \verbatim +*> LWORK is INTEGER +*> Length of CWORK to confirm proper allocation of workspace. +*> LWORK depends on the job: +*> +*> 1. If only SIGMA is needed ( JOBU = 'N', JOBV = 'N' ) and +*> 1.1 .. no scaled condition estimate required (JOBA.NE.'E'.AND.JOBA.NE.'G'): +*> LWORK >= 2*N+1. This is the minimal requirement. +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= N + (N+1)*NB. Here NB is the optimal +*> block size for ZGEQP3 and ZGEQRF. +*> In general, optimal LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3),N+LWORK(ZGEQRF), LWORK(ZGESVJ)). +*> 1.2. .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). In this case, LWORK the minimal +*> requirement is LWORK >= N*N + 2*N. +*> ->> For optimal performance (blocked code) the optimal value +*> is LWORK >= max(N+(N+1)*NB, N*N+2*N)=N**2+2*N. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3),N+LWORK(ZGEQRF), LWORK(ZGESVJ), +*> N*N+LWORK(ZPOCON)). +*> 2. If SIGMA and the right singular vectors are needed (JOBV = 'V'), +*> (JOBU = 'N') +*> 2.1 .. no scaled condition estimate requested (JOBE = 'N'): +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance, +*> LWORK >= max(N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for ZGEQP3, ZGEQRF, ZGELQF, +*> ZUNMLQ. In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3), N+LWORK(ZGESVJ), +*> N+LWORK(ZGELQF), 2*N+LWORK(ZGEQRF), N+LWORK(ZUNMLQ)). +*> 2.2 .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance, +*> LWORK >= max(N+(N+1)*NB, 2*N,2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for ZGEQP3, ZGEQRF, ZGELQF, +*> ZUNMLQ. In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3), LWORK(ZPOCON), N+LWORK(ZGESVJ), +*> N+LWORK(ZGELQF), 2*N+LWORK(ZGEQRF), N+LWORK(ZUNMLQ)). +*> 3. If SIGMA and the left singular vectors are needed +*> 3.1 .. no scaled condition estimate requested (JOBE = 'N'): +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance: +*> if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for ZGEQP3, ZGEQRF, ZUNMQR. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3), 2*N+LWORK(ZGEQRF), N+LWORK(ZUNMQR)). +*> 3.2 .. an estimate of the scaled condition number of A is +*> required (JOBA='E', or 'G'). +*> -> the minimal requirement is LWORK >= 3*N. +*> -> For optimal performance: +*> if JOBU = 'U' :: LWORK >= max(3*N, N+(N+1)*NB, 2*N+N*NB)=2*N+N*NB, +*> where NB is the optimal block size for ZGEQP3, ZGEQRF, ZUNMQR. +*> In general, the optimal length LWORK is computed as +*> LWORK >= max(N+LWORK(ZGEQP3),N+LWORK(ZPOCON), +*> 2*N+LWORK(ZGEQRF), N+LWORK(ZUNMQR)). +*> 4. If the full SVD is needed: (JOBU = 'U' or JOBU = 'F') and +*> 4.1. if JOBV = 'V' +*> the minimal requirement is LWORK >= 5*N+2*N*N. +*> 4.2. if JOBV = 'J' the minimal requirement is +*> LWORK >= 4*N+N*N. +*> In both cases, the allocated CWORK can accommodate blocked runs +*> of ZGEQP3, ZGEQRF, ZGELQF, SUNMQR, ZUNMLQ. +*> +*> If the call to ZGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit CWORK(1) contains the optimal and CWORK(2) contains the +*> minimal length of CWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is DOUBLE PRECISION array, dimension (MAX(7,LRWORK)) +*> On exit, +*> RWORK(1) = Determines the scaling factor SCALE = RWORK(2) / RWORK(1) +*> such that SCALE*SVA(1:N) are the computed singular values +*> of A. (See the description of SVA().) +*> RWORK(2) = See the description of RWORK(1). +*> RWORK(3) = SCONDA is an estimate for the condition number of +*> column equilibrated A. (If JOBA = 'E' or 'G') +*> SCONDA is an estimate of SQRT(||(R^* * R)^(-1)||_1). +*> It is computed using ZPOCON. It holds +*> N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA +*> where R is the triangular factor from the QRF of A. +*> However, if R is truncated and the numerical rank is +*> determined to be strictly smaller than N, SCONDA is +*> returned as -1, thus indicating that the smallest +*> singular values might be lost. +*> +*> If full SVD is needed, the following two condition numbers are +*> useful for the analysis of the algorithm. They are provided for +*> a developer/implementer who is familiar with the details of +*> the method. +*> +*> RWORK(4) = an estimate of the scaled condition number of the +*> triangular factor in the first QR factorization. +*> RWORK(5) = an estimate of the scaled condition number of the +*> triangular factor in the second QR factorization. +*> The following two parameters are computed if JOBT = 'T'. +*> They are provided for a developer/implementer who is familiar +*> with the details of the method. +*> RWORK(6) = the entropy of A^* * A :: this is the Shannon entropy +*> of diag(A^* * A) / Trace(A^* * A) taken as point in the +*> probability simplex. +*> RWORK(7) = the entropy of A * A^*. (See the description of RWORK(6).) +*> If the call to ZGEJSV is a workspace query (indicated by LWORK=-1 or +*> LRWORK=-1), then on exit RWORK(1) contains the required length of +*> RWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[in] LRWORK +*> \verbatim +*> LRWORK is INTEGER +*> Length of RWORK to confirm proper allocation of workspace. +*> LRWORK depends on the job: +*> +*> 1. If only the singular values are requested i.e. if +*> LSAME(JOBU,'N') .AND. LSAME(JOBV,'N') +*> then: +*> 1.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then: LRWORK = max( 7, 2 * M ). +*> 1.2. Otherwise, LRWORK = max( 7, N ). +*> 2. If singular values with the right singular vectors are requested +*> i.e. if +*> (LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) .AND. +*> .NOT.(LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) +*> then: +*> 2.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 2.2. Otherwise, LRWORK = max( 7, N ). +*> 3. If singular values with the left singular vectors are requested, i.e. if +*> (LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND. +*> .NOT.(LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) +*> then: +*> 3.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 3.2. Otherwise, LRWORK = max( 7, N ). +*> 4. If singular values with both the left and the right singular vectors +*> are requested, i.e. if +*> (LSAME(JOBU,'U').OR.LSAME(JOBU,'F')) .AND. +*> (LSAME(JOBV,'V').OR.LSAME(JOBV,'J')) +*> then: +*> 4.1. If LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G'), +*> then LRWORK = max( 7, 2 * M ). +*> 4.2. Otherwise, LRWORK = max( 7, N ). +*> +*> If, on entry, LRWORK = -1 or LWORK=-1, a workspace query is assumed and +*> the length of RWORK is returned in RWORK(1). +*> \endverbatim +*> +*> \param[out] IWORK +*> \verbatim +*> IWORK is INTEGER array, of dimension at least 4, that further depends +*> on the job: +*> +*> 1. If only the singular values are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 2. If the singular values and the right singular vectors are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 3. If the singular values and the left singular vectors are requested then: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 4. If the singular values with both the left and the right singular vectors +*> are requested, then: +*> 4.1. If LSAME(JOBV,'J') the length of IWORK is determined as follows: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is N+M; otherwise the length of IWORK is N. +*> 4.2. If LSAME(JOBV,'V') the length of IWORK is determined as follows: +*> If ( LSAME(JOBT,'T') .OR. LSAME(JOBA,'F') .OR. LSAME(JOBA,'G') ) +*> then the length of IWORK is 2*N+M; otherwise the length of IWORK is 2*N. +*> +*> On exit, +*> IWORK(1) = the numerical rank determined after the initial +*> QR factorization with pivoting. See the descriptions +*> of JOBA and JOBR. +*> IWORK(2) = the number of the computed nonzero singular values +*> IWORK(3) = if nonzero, a warning message: +*> If IWORK(3) = 1 then some of the column norms of A +*> were denormalized floats. The requested high accuracy +*> is not warranted by the data. +*> IWORK(4) = 1 or -1. If IWORK(4) = 1, then the procedure used A^* to +*> do the job as specified by the JOB parameters. +*> If the call to ZGEJSV is a workspace query (indicated by LWORK = -1 or +*> LRWORK = -1), then on exit IWORK(1) contains the required length of +*> IWORK for the job parameters used in the call. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> < 0: if INFO = -i, then the i-th argument had an illegal value. +*> = 0: successful exit; +*> > 0: ZGEJSV did not converge in the maximal allowed number +*> of sweeps. The computed values may be inaccurate. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complex16GEsing +* +*> \par Further Details: +* ===================== +*> +*> \verbatim +*> +*> ZGEJSV implements a preconditioned Jacobi SVD algorithm. It uses ZGEQP3, +*> ZGEQRF, and ZGELQF as preprocessors and preconditioners. Optionally, an +*> additional row pivoting can be used as a preprocessor, which in some +*> cases results in much higher accuracy. An example is matrix A with the +*> structure A = D1 * C * D2, where D1, D2 are arbitrarily ill-conditioned +*> diagonal matrices and C is well-conditioned matrix. In that case, complete +*> pivoting in the first QR factorizations provides accuracy dependent on the +*> condition number of C, and independent of D1, D2. Such higher accuracy is +*> not completely understood theoretically, but it works well in practice. +*> Further, if A can be written as A = B*D, with well-conditioned B and some +*> diagonal D, then the high accuracy is guaranteed, both theoretically and +*> in software, independent of D. For more details see [1], [2]. +*> The computational range for the singular values can be the full range +*> ( UNDERFLOW,OVERFLOW ), provided that the machine arithmetic and the BLAS +*> & LAPACK routines called by ZGEJSV are implemented to work in that range. +*> If that is not the case, then the restriction for safe computation with +*> the singular values in the range of normalized IEEE numbers is that the +*> spectral condition number kappa(A)=sigma_max(A)/sigma_min(A) does not +*> overflow. This code (ZGEJSV) is best used in this restricted range, +*> meaning that singular values of magnitude below ||A||_2 / DLAMCH('O') are +*> returned as zeros. See JOBR for details on this. +*> Further, this implementation is somewhat slower than the one described +*> in [1,2] due to replacement of some non-LAPACK components, and because +*> the choice of some tuning parameters in the iterative part (ZGESVJ) is +*> left to the implementer on a particular machine. +*> The rank revealing QR factorization (in this code: ZGEQP3) should be +*> implemented as in [3]. We have a new version of ZGEQP3 under development +*> that is more robust than the current one in LAPACK, with a cleaner cut in +*> rank deficient cases. It will be available in the SIGMA library [4]. +*> If M is much larger than N, it is obvious that the initial QRF with +*> column pivoting can be preprocessed by the QRF without pivoting. That +*> well known trick is not used in ZGEJSV because in some cases heavy row +*> weighting can be treated with complete pivoting. The overhead in cases +*> M much larger than N is then only due to pivoting, but the benefits in +*> terms of accuracy have prevailed. The implementer/user can incorporate +*> this extra QRF step easily. The implementer can also improve data movement +*> (matrix transpose, matrix copy, matrix transposed copy) - this +*> implementation of ZGEJSV uses only the simplest, naive data movement. +*> \endverbatim +* +*> \par Contributor: +* ================== +*> +*> Zlatko Drmac, Department of Mathematics, Faculty of Science, +*> University of Zagreb (Zagreb, Croatia); drmac@math.hr +* +*> \par References: +* ================ +*> +*> \verbatim +*> +*> [1] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. +*> LAPACK Working note 169. +*> [2] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. +*> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. +*> LAPACK Working note 170. +*> [3] Z. Drmac and Z. Bujanovic: On the failure of rank-revealing QR +*> factorization software - a case study. +*> ACM Trans. Math. Softw. Vol. 35, No 2 (2008), pp. 1-28. +*> LAPACK Working note 176. +*> [4] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, +*> QSVD, (H,K)-SVD computations. +*> Department of Mathematics, University of Zagreb, 2008, 2016. +*> \endverbatim +* +*> \par Bugs, examples and comments: +* ================================= +*> +*> Please report all bugs and send interesting examples and/or comments to +*> drmac@math.hr. Thank you. +*> +* ===================================================================== + SUBROUTINE ZGEJSV( JOBA, JOBU, JOBV, JOBR, JOBT, JOBP, + $ M, N, A, LDA, SVA, U, LDU, V, LDV, + $ CWORK, LWORK, RWORK, LRWORK, IWORK, INFO ) +* +* -- LAPACK computational routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + IMPLICIT NONE + INTEGER INFO, LDA, LDU, LDV, LWORK, LRWORK, M, N +* .. +* .. Array Arguments .. + COMPLEX*16 A( LDA, * ), U( LDU, * ), V( LDV, * ), + $ CWORK( LWORK ) + DOUBLE PRECISION SVA( N ), RWORK( LRWORK ) + INTEGER IWORK( * ) + CHARACTER*1 JOBA, JOBP, JOBR, JOBT, JOBU, JOBV +* .. +* +* =========================================================================== +* +* .. Local Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D0, ONE = 1.0D0 ) + COMPLEX*16 CZERO, CONE + PARAMETER ( CZERO = ( 0.0D0, 0.0D0 ), CONE = ( 1.0D0, 0.0D0 ) ) +* .. +* .. Local Scalars .. + COMPLEX*16 CTEMP + DOUBLE PRECISION AAPP, AAQQ, AATMAX, AATMIN, BIG, BIG1, + $ COND_OK, CONDR1, CONDR2, ENTRA, ENTRAT, EPSLN, + $ MAXPRJ, SCALEM, SCONDA, SFMIN, SMALL, TEMP1, + $ USCAL1, USCAL2, XSC + INTEGER IERR, N1, NR, NUMRANK, p, q, WARNING + LOGICAL ALMORT, DEFR, ERREST, GOSCAL, JRACC, KILL, LQUERY, + $ LSVEC, L2ABER, L2KILL, L2PERT, L2RANK, L2TRAN, NOSCAL, + $ ROWPIV, RSVEC, TRANSP +* + INTEGER OPTWRK, MINWRK, MINRWRK, MINIWRK + INTEGER LWCON, LWLQF, LWQP3, LWQRF, LWUNMLQ, LWUNMQR, LWUNMQRM, + $ LWSVDJ, LWSVDJV, LRWQP3, LRWCON, LRWSVDJ, IWOFF + INTEGER LWRK_ZGELQF, LWRK_ZGEQP3, LWRK_ZGEQP3N, LWRK_ZGEQRF, + $ LWRK_ZGESVJ, LWRK_ZGESVJV, LWRK_ZGESVJU, LWRK_ZUNMLQ, + $ LWRK_ZUNMQR, LWRK_ZUNMQRM +* .. +* .. Local Arrays + COMPLEX*16 CDUMMY(1) + DOUBLE PRECISION RDUMMY(1) +* +* .. Intrinsic Functions .. + INTRINSIC ABS, DCMPLX, CONJG, DLOG, MAX, MIN, DBLE, NINT, SQRT +* .. +* .. External Functions .. + DOUBLE PRECISION DLAMCH, DZNRM2 + INTEGER IDAMAX, IZAMAX + LOGICAL LSAME + EXTERNAL IDAMAX, IZAMAX, LSAME, DLAMCH, DZNRM2 +* .. +* .. External Subroutines .. + EXTERNAL DLASSQ, ZCOPY, ZGELQF, ZGEQP3, ZGEQRF, ZLACPY, ZLAPMR, + $ ZLASCL, DLASCL, ZLASET, ZLASSQ, ZLASWP, ZUNGQR, ZUNMLQ, + $ ZUNMQR, ZPOCON, DSCAL, ZDSCAL, ZSWAP, ZTRSM, ZLACGV, + $ XERBLA +* + EXTERNAL ZGESVJ +* .. +* +* Test the input arguments +* + LSVEC = LSAME( JOBU, 'U' ) .OR. LSAME( JOBU, 'F' ) + JRACC = LSAME( JOBV, 'J' ) + RSVEC = LSAME( JOBV, 'V' ) .OR. JRACC + ROWPIV = LSAME( JOBA, 'F' ) .OR. LSAME( JOBA, 'G' ) + L2RANK = LSAME( JOBA, 'R' ) + L2ABER = LSAME( JOBA, 'A' ) + ERREST = LSAME( JOBA, 'E' ) .OR. LSAME( JOBA, 'G' ) + L2TRAN = LSAME( JOBT, 'T' ) .AND. ( M .EQ. N ) + L2KILL = LSAME( JOBR, 'R' ) + DEFR = LSAME( JOBR, 'N' ) + L2PERT = LSAME( JOBP, 'P' ) +* + LQUERY = ( LWORK .EQ. -1 ) .OR. ( LRWORK .EQ. -1 ) +* + IF ( .NOT.(ROWPIV .OR. L2RANK .OR. L2ABER .OR. + $ ERREST .OR. LSAME( JOBA, 'C' ) )) THEN + INFO = - 1 + ELSE IF ( .NOT.( LSVEC .OR. LSAME( JOBU, 'N' ) .OR. + $ ( LSAME( JOBU, 'W' ) .AND. RSVEC .AND. L2TRAN ) ) ) THEN + INFO = - 2 + ELSE IF ( .NOT.( RSVEC .OR. LSAME( JOBV, 'N' ) .OR. + $ ( LSAME( JOBV, 'W' ) .AND. LSVEC .AND. L2TRAN ) ) ) THEN + INFO = - 3 + ELSE IF ( .NOT. ( L2KILL .OR. DEFR ) ) THEN + INFO = - 4 + ELSE IF ( .NOT. ( LSAME(JOBT,'T') .OR. LSAME(JOBT,'N') ) ) THEN + INFO = - 5 + ELSE IF ( .NOT. ( L2PERT .OR. LSAME( JOBP, 'N' ) ) ) THEN + INFO = - 6 + ELSE IF ( M .LT. 0 ) THEN + INFO = - 7 + ELSE IF ( ( N .LT. 0 ) .OR. ( N .GT. M ) ) THEN + INFO = - 8 + ELSE IF ( LDA .LT. M ) THEN + INFO = - 10 + ELSE IF ( LSVEC .AND. ( LDU .LT. M ) ) THEN + INFO = - 13 + ELSE IF ( RSVEC .AND. ( LDV .LT. N ) ) THEN + INFO = - 15 + ELSE +* #:) + INFO = 0 + END IF +* + IF ( INFO .EQ. 0 ) THEN +* .. compute the minimal and the optimal workspace lengths +* [[The expressions for computing the minimal and the optimal +* values of LCWORK, LRWORK are written with a lot of redundancy and +* can be simplified. However, this verbose form is useful for +* maintenance and modifications of the code.]] +* +* .. minimal workspace length for ZGEQP3 of an M x N matrix, +* ZGEQRF of an N x N matrix, ZGELQF of an N x N matrix, +* ZUNMLQ for computing N x N matrix, ZUNMQR for computing N x N +* matrix, ZUNMQR for computing M x N matrix, respectively. + LWQP3 = N+1 + LWQRF = MAX( 1, N ) + LWLQF = MAX( 1, N ) + LWUNMLQ = MAX( 1, N ) + LWUNMQR = MAX( 1, N ) + LWUNMQRM = MAX( 1, M ) +* .. minimal workspace length for ZPOCON of an N x N matrix + LWCON = 2 * N +* .. minimal workspace length for ZGESVJ of an N x N matrix, +* without and with explicit accumulation of Jacobi rotations + LWSVDJ = MAX( 2 * N, 1 ) + LWSVDJV = MAX( 2 * N, 1 ) +* .. minimal REAL workspace length for ZGEQP3, ZPOCON, ZGESVJ + LRWQP3 = 2 * N + LRWCON = N + LRWSVDJ = N + IF ( LQUERY ) THEN + CALL ZGEQP3( M, N, A, LDA, IWORK, CDUMMY, CDUMMY, -1, + $ RDUMMY, IERR ) + LWRK_ZGEQP3 = INT( CDUMMY(1) ) + CALL ZGEQRF( N, N, A, LDA, CDUMMY, CDUMMY,-1, IERR ) + LWRK_ZGEQRF = INT( CDUMMY(1) ) + CALL ZGELQF( N, N, A, LDA, CDUMMY, CDUMMY,-1, IERR ) + LWRK_ZGELQF = INT( CDUMMY(1) ) + END IF + MINWRK = 2 + OPTWRK = 2 + MINIWRK = N + IF ( .NOT. (LSVEC .OR. RSVEC ) ) THEN +* .. minimal and optimal sizes of the complex workspace if +* only the singular values are requested + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N**2+LWCON, N+LWQRF, LWSVDJ ) + ELSE + MINWRK = MAX( N+LWQP3, N+LWQRF, LWSVDJ ) + END IF + IF ( LQUERY ) THEN + CALL ZGESVJ( 'L', 'N', 'N', N, N, A, LDA, SVA, N, V, + $ LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_ZGEQP3, N**2+LWCON, + $ N+LWRK_ZGEQRF, LWRK_ZGESVJ ) + ELSE + OPTWRK = MAX( N+LWRK_ZGEQP3, N+LWRK_ZGEQRF, + $ LWRK_ZGESVJ ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWCON, LRWSVDJ ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWCON, LRWSVDJ ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE IF ( RSVEC .AND. (.NOT.LSVEC) ) THEN +* .. minimal and optimal sizes of the complex workspace if the +* singular values and the right singular vectors are requested + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, LWCON, LWSVDJ, N+LWLQF, + $ 2*N+LWQRF, N+LWSVDJ, N+LWUNMLQ ) + ELSE + MINWRK = MAX( N+LWQP3, LWSVDJ, N+LWLQF, 2*N+LWQRF, + $ N+LWSVDJ, N+LWUNMLQ ) + END IF + IF ( LQUERY ) THEN + CALL ZGESVJ( 'L', 'U', 'N', N,N, U, LDU, SVA, N, A, + $ LDA, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJ = INT( CDUMMY(1) ) + CALL ZUNMLQ( 'L', 'C', N, N, N, A, LDA, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_ZUNMLQ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_ZGEQP3, LWCON, LWRK_ZGESVJ, + $ N+LWRK_ZGELQF, 2*N+LWRK_ZGEQRF, + $ N+LWRK_ZGESVJ, N+LWRK_ZUNMLQ ) + ELSE + OPTWRK = MAX( N+LWRK_ZGEQP3, LWRK_ZGESVJ,N+LWRK_ZGELQF, + $ 2*N+LWRK_ZGEQRF, N+LWRK_ZGESVJ, + $ N+LWRK_ZUNMLQ ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE IF ( LSVEC .AND. (.NOT.RSVEC) ) THEN +* .. minimal and optimal sizes of the complex workspace if the +* singular values and the left singular vectors are requested + IF ( ERREST ) THEN + MINWRK = N + MAX( LWQP3,LWCON,N+LWQRF,LWSVDJ,LWUNMQRM ) + ELSE + MINWRK = N + MAX( LWQP3, N+LWQRF, LWSVDJ, LWUNMQRM ) + END IF + IF ( LQUERY ) THEN + CALL ZGESVJ( 'L', 'U', 'N', N,N, U, LDU, SVA, N, A, + $ LDA, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJ = INT( CDUMMY(1) ) + CALL ZUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_ZUNMQRM = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = N + MAX( LWRK_ZGEQP3, LWCON, N+LWRK_ZGEQRF, + $ LWRK_ZGESVJ, LWRK_ZUNMQRM ) + ELSE + OPTWRK = N + MAX( LWRK_ZGEQP3, N+LWRK_ZGEQRF, + $ LWRK_ZGESVJ, LWRK_ZUNMQRM ) + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + IF ( ERREST ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ ) + END IF + ELSE + IF ( ERREST ) THEN + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ ) + END IF + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE +* .. minimal and optimal sizes of the complex workspace if the +* full SVD is requested + IF ( .NOT. JRACC ) THEN + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N+LWCON, 2*N+N**2+LWCON, + $ 2*N+LWQRF, 2*N+LWQP3, + $ 2*N+N**2+N+LWLQF, 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWSVDJ, 2*N+N**2+N+LWSVDJV, + $ 2*N+N**2+N+LWUNMQR,2*N+N**2+N+LWUNMLQ, + $ N+N**2+LWSVDJ, N+LWUNMQRM ) + ELSE + MINWRK = MAX( N+LWQP3, 2*N+N**2+LWCON, + $ 2*N+LWQRF, 2*N+LWQP3, + $ 2*N+N**2+N+LWLQF, 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWSVDJ, 2*N+N**2+N+LWSVDJV, + $ 2*N+N**2+N+LWUNMQR,2*N+N**2+N+LWUNMLQ, + $ N+N**2+LWSVDJ, N+LWUNMQRM ) + END IF + MINIWRK = MINIWRK + N + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + ELSE + IF ( ERREST ) THEN + MINWRK = MAX( N+LWQP3, N+LWCON, 2*N+LWQRF, + $ 2*N+N**2+LWSVDJV, 2*N+N**2+N+LWUNMQR, + $ N+LWUNMQRM ) + ELSE + MINWRK = MAX( N+LWQP3, 2*N+LWQRF, + $ 2*N+N**2+LWSVDJV, 2*N+N**2+N+LWUNMQR, + $ N+LWUNMQRM ) + END IF + IF ( ROWPIV .OR. L2TRAN ) MINIWRK = MINIWRK + M + END IF + IF ( LQUERY ) THEN + CALL ZUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_ZUNMQRM = INT( CDUMMY(1) ) + CALL ZUNMQR( 'L', 'N', N, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_ZUNMQR = INT( CDUMMY(1) ) + IF ( .NOT. JRACC ) THEN + CALL ZGEQP3( N,N, A, LDA, IWORK, CDUMMY,CDUMMY, -1, + $ RDUMMY, IERR ) + LWRK_ZGEQP3N = INT( CDUMMY(1) ) + CALL ZGESVJ( 'L', 'U', 'N', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJ = INT( CDUMMY(1) ) + CALL ZGESVJ( 'U', 'U', 'N', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJU = INT( CDUMMY(1) ) + CALL ZGESVJ( 'L', 'U', 'V', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJV = INT( CDUMMY(1) ) + CALL ZUNMLQ( 'L', 'C', N, N, N, A, LDA, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_ZUNMLQ = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_ZGEQP3, N+LWCON, + $ 2*N+N**2+LWCON, 2*N+LWRK_ZGEQRF, + $ 2*N+LWRK_ZGEQP3N, + $ 2*N+N**2+N+LWRK_ZGELQF, + $ 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWRK_ZGESVJ, + $ 2*N+N**2+N+LWRK_ZGESVJV, + $ 2*N+N**2+N+LWRK_ZUNMQR, + $ 2*N+N**2+N+LWRK_ZUNMLQ, + $ N+N**2+LWRK_ZGESVJU, + $ N+LWRK_ZUNMQRM ) + ELSE + OPTWRK = MAX( N+LWRK_ZGEQP3, + $ 2*N+N**2+LWCON, 2*N+LWRK_ZGEQRF, + $ 2*N+LWRK_ZGEQP3N, + $ 2*N+N**2+N+LWRK_ZGELQF, + $ 2*N+N**2+N+N**2+LWCON, + $ 2*N+N**2+N+LWRK_ZGESVJ, + $ 2*N+N**2+N+LWRK_ZGESVJV, + $ 2*N+N**2+N+LWRK_ZUNMQR, + $ 2*N+N**2+N+LWRK_ZUNMLQ, + $ N+N**2+LWRK_ZGESVJU, + $ N+LWRK_ZUNMQRM ) + END IF + ELSE + CALL ZGESVJ( 'L', 'U', 'V', N, N, U, LDU, SVA, + $ N, V, LDV, CDUMMY, -1, RDUMMY, -1, IERR ) + LWRK_ZGESVJV = INT( CDUMMY(1) ) + CALL ZUNMQR( 'L', 'N', N, N, N, CDUMMY, N, CDUMMY, + $ V, LDV, CDUMMY, -1, IERR ) + LWRK_ZUNMQR = INT( CDUMMY(1) ) + CALL ZUNMQR( 'L', 'N', M, N, N, A, LDA, CDUMMY, U, + $ LDU, CDUMMY, -1, IERR ) + LWRK_ZUNMQRM = INT( CDUMMY(1) ) + IF ( ERREST ) THEN + OPTWRK = MAX( N+LWRK_ZGEQP3, N+LWCON, + $ 2*N+LWRK_ZGEQRF, 2*N+N**2, + $ 2*N+N**2+LWRK_ZGESVJV, + $ 2*N+N**2+N+LWRK_ZUNMQR,N+LWRK_ZUNMQRM ) + ELSE + OPTWRK = MAX( N+LWRK_ZGEQP3, 2*N+LWRK_ZGEQRF, + $ 2*N+N**2, 2*N+N**2+LWRK_ZGESVJV, + $ 2*N+N**2+N+LWRK_ZUNMQR, + $ N+LWRK_ZUNMQRM ) + END IF + END IF + END IF + IF ( L2TRAN .OR. ROWPIV ) THEN + MINRWRK = MAX( 7, 2*M, LRWQP3, LRWSVDJ, LRWCON ) + ELSE + MINRWRK = MAX( 7, LRWQP3, LRWSVDJ, LRWCON ) + END IF + END IF + MINWRK = MAX( 2, MINWRK ) + OPTWRK = MAX( MINWRK, OPTWRK ) + IF ( LWORK .LT. MINWRK .AND. (.NOT.LQUERY) ) INFO = - 17 + IF ( LRWORK .LT. MINRWRK .AND. (.NOT.LQUERY) ) INFO = - 19 + END IF +* + IF ( INFO .NE. 0 ) THEN +* #:( + CALL XERBLA( 'ZGEJSV', - INFO ) + RETURN + ELSE IF ( LQUERY ) THEN + CWORK(1) = OPTWRK + CWORK(2) = MINWRK + RWORK(1) = MINRWRK + IWORK(1) = MAX( 4, MINIWRK ) + RETURN + END IF +* +* Quick return for void matrix (Y3K safe) +* #:) + IF ( ( M .EQ. 0 ) .OR. ( N .EQ. 0 ) ) THEN + IWORK(1:4) = 0 + RWORK(1:7) = 0 + RETURN + ENDIF +* +* Determine whether the matrix U should be M x N or M x M +* + IF ( LSVEC ) THEN + N1 = N + IF ( LSAME( JOBU, 'F' ) ) N1 = M + END IF +* +* Set numerical parameters +* +*! NOTE: Make sure DLAMCH() does not fail on the target architecture. +* + EPSLN = DLAMCH('Epsilon') + SFMIN = DLAMCH('SafeMinimum') + SMALL = SFMIN / EPSLN + BIG = DLAMCH('O') +* BIG = ONE / SFMIN +* +* Initialize SVA(1:N) = diag( ||A e_i||_2 )_1^N +* +*(!) If necessary, scale SVA() to protect the largest norm from +* overflow. It is possible that this scaling pushes the smallest +* column norm left from the underflow threshold (extreme case). +* + SCALEM = ONE / SQRT(DBLE(M)*DBLE(N)) + NOSCAL = .TRUE. + GOSCAL = .TRUE. + DO 1874 p = 1, N + AAPP = ZERO + AAQQ = ONE + CALL ZLASSQ( M, A(1,p), 1, AAPP, AAQQ ) + IF ( AAPP .GT. BIG ) THEN + INFO = - 9 + CALL XERBLA( 'ZGEJSV', -INFO ) + RETURN + END IF + AAQQ = SQRT(AAQQ) + IF ( ( AAPP .LT. (BIG / AAQQ) ) .AND. NOSCAL ) THEN + SVA(p) = AAPP * AAQQ + ELSE + NOSCAL = .FALSE. + SVA(p) = AAPP * ( AAQQ * SCALEM ) + IF ( GOSCAL ) THEN + GOSCAL = .FALSE. + CALL DSCAL( p-1, SCALEM, SVA, 1 ) + END IF + END IF + 1874 CONTINUE +* + IF ( NOSCAL ) SCALEM = ONE +* + AAPP = ZERO + AAQQ = BIG + DO 4781 p = 1, N + AAPP = MAX( AAPP, SVA(p) ) + IF ( SVA(p) .NE. ZERO ) AAQQ = MIN( AAQQ, SVA(p) ) + 4781 CONTINUE +* +* Quick return for zero M x N matrix +* #:) + IF ( AAPP .EQ. ZERO ) THEN + IF ( LSVEC ) CALL ZLASET( 'G', M, N1, CZERO, CONE, U, LDU ) + IF ( RSVEC ) CALL ZLASET( 'G', N, N, CZERO, CONE, V, LDV ) + RWORK(1) = ONE + RWORK(2) = ONE + IF ( ERREST ) RWORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = ONE + RWORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ZERO + RWORK(7) = ZERO + END IF + IWORK(1) = 0 + IWORK(2) = 0 + IWORK(3) = 0 + IWORK(4) = -1 + RETURN + END IF +* +* Issue warning if denormalized column norms detected. Override the +* high relative accuracy request. Issue licence to kill nonzero columns +* (set them to zero) whose norm is less than sigma_max / BIG (roughly). +* #:( + WARNING = 0 + IF ( AAQQ .LE. SFMIN ) THEN + L2RANK = .TRUE. + L2KILL = .TRUE. + WARNING = 1 + END IF +* +* Quick return for one-column matrix +* #:) + IF ( N .EQ. 1 ) THEN +* + IF ( LSVEC ) THEN + CALL ZLASCL( 'G',0,0,SVA(1),SCALEM, M,1,A(1,1),LDA,IERR ) + CALL ZLACPY( 'A', M, 1, A, LDA, U, LDU ) +* computing all M left singular vectors of the M x 1 matrix + IF ( N1 .NE. N ) THEN + CALL ZGEQRF( M, N, U,LDU, CWORK, CWORK(N+1),LWORK-N,IERR ) + CALL ZUNGQR( M,N1,1, U,LDU,CWORK,CWORK(N+1),LWORK-N,IERR ) + CALL ZCOPY( M, A(1,1), 1, U(1,1), 1 ) + END IF + END IF + IF ( RSVEC ) THEN + V(1,1) = CONE + END IF + IF ( SVA(1) .LT. (BIG*SCALEM) ) THEN + SVA(1) = SVA(1) / SCALEM + SCALEM = ONE + END IF + RWORK(1) = ONE / SCALEM + RWORK(2) = ONE + IF ( SVA(1) .NE. ZERO ) THEN + IWORK(1) = 1 + IF ( ( SVA(1) / SCALEM) .GE. SFMIN ) THEN + IWORK(2) = 1 + ELSE + IWORK(2) = 0 + END IF + ELSE + IWORK(1) = 0 + IWORK(2) = 0 + END IF + IWORK(3) = 0 + IWORK(4) = -1 + IF ( ERREST ) RWORK(3) = ONE + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = ONE + RWORK(5) = ONE + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ZERO + RWORK(7) = ZERO + END IF + RETURN +* + END IF +* + TRANSP = .FALSE. +* + AATMAX = -ONE + AATMIN = BIG + IF ( ROWPIV .OR. L2TRAN ) THEN +* +* Compute the row norms, needed to determine row pivoting sequence +* (in the case of heavily row weighted A, row pivoting is strongly +* advised) and to collect information needed to compare the +* structures of A * A^* and A^* * A (in the case L2TRAN.EQ..TRUE.). +* + IF ( L2TRAN ) THEN + DO 1950 p = 1, M + XSC = ZERO + TEMP1 = ONE + CALL ZLASSQ( N, A(p,1), LDA, XSC, TEMP1 ) +* ZLASSQ gets both the ell_2 and the ell_infinity norm +* in one pass through the vector + RWORK(M+p) = XSC * SCALEM + RWORK(p) = XSC * (SCALEM*SQRT(TEMP1)) + AATMAX = MAX( AATMAX, RWORK(p) ) + IF (RWORK(p) .NE. ZERO) + $ AATMIN = MIN(AATMIN,RWORK(p)) + 1950 CONTINUE + ELSE + DO 1904 p = 1, M + RWORK(M+p) = SCALEM*ABS( A(p,IZAMAX(N,A(p,1),LDA)) ) + AATMAX = MAX( AATMAX, RWORK(M+p) ) + AATMIN = MIN( AATMIN, RWORK(M+p) ) + 1904 CONTINUE + END IF +* + END IF +* +* For square matrix A try to determine whether A^* would be better +* input for the preconditioned Jacobi SVD, with faster convergence. +* The decision is based on an O(N) function of the vector of column +* and row norms of A, based on the Shannon entropy. This should give +* the right choice in most cases when the difference actually matters. +* It may fail and pick the slower converging side. +* + ENTRA = ZERO + ENTRAT = ZERO + IF ( L2TRAN ) THEN +* + XSC = ZERO + TEMP1 = ONE + CALL DLASSQ( N, SVA, 1, XSC, TEMP1 ) + TEMP1 = ONE / TEMP1 +* + ENTRA = ZERO + DO 1113 p = 1, N + BIG1 = ( ( SVA(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRA = ENTRA + BIG1 * DLOG(BIG1) + 1113 CONTINUE + ENTRA = - ENTRA / DLOG(DBLE(N)) +* +* Now, SVA().^2/Trace(A^* * A) is a point in the probability simplex. +* It is derived from the diagonal of A^* * A. Do the same with the +* diagonal of A * A^*, compute the entropy of the corresponding +* probability distribution. Note that A * A^* and A^* * A have the +* same trace. +* + ENTRAT = ZERO + DO 1114 p = 1, M + BIG1 = ( ( RWORK(p) / XSC )**2 ) * TEMP1 + IF ( BIG1 .NE. ZERO ) ENTRAT = ENTRAT + BIG1 * DLOG(BIG1) + 1114 CONTINUE + ENTRAT = - ENTRAT / DLOG(DBLE(M)) +* +* Analyze the entropies and decide A or A^*. Smaller entropy +* usually means better input for the algorithm. +* + TRANSP = ( ENTRAT .LT. ENTRA ) +* +* If A^* is better than A, take the adjoint of A. This is allowed +* only for square matrices, M=N. + IF ( TRANSP ) THEN +* In an optimal implementation, this trivial transpose +* should be replaced with faster transpose. + DO 1115 p = 1, N - 1 + A(p,p) = CONJG(A(p,p)) + DO 1116 q = p + 1, N + CTEMP = CONJG(A(q,p)) + A(q,p) = CONJG(A(p,q)) + A(p,q) = CTEMP + 1116 CONTINUE + 1115 CONTINUE + A(N,N) = CONJG(A(N,N)) + DO 1117 p = 1, N + RWORK(M+p) = SVA(p) + SVA(p) = RWORK(p) +* previously computed row 2-norms are now column 2-norms +* of the transposed matrix + 1117 CONTINUE + TEMP1 = AAPP + AAPP = AATMAX + AATMAX = TEMP1 + TEMP1 = AAQQ + AAQQ = AATMIN + AATMIN = TEMP1 + KILL = LSVEC + LSVEC = RSVEC + RSVEC = KILL + IF ( LSVEC ) N1 = N +* + ROWPIV = .TRUE. + END IF +* + END IF +* END IF L2TRAN +* +* Scale the matrix so that its maximal singular value remains less +* than SQRT(BIG) -- the matrix is scaled so that its maximal column +* has Euclidean norm equal to SQRT(BIG/N). The only reason to keep +* SQRT(BIG) instead of BIG is the fact that ZGEJSV uses LAPACK and +* BLAS routines that, in some implementations, are not capable of +* working in the full interval [SFMIN,BIG] and that they may provoke +* overflows in the intermediate results. If the singular values spread +* from SFMIN to BIG, then ZGESVJ will compute them. So, in that case, +* one should use ZGESVJ instead of ZGEJSV. +* >> change in the April 2016 update: allow bigger range, i.e. the +* largest column is allowed up to BIG/N and ZGESVJ will do the rest. + BIG1 = SQRT( BIG ) + TEMP1 = SQRT( BIG / DBLE(N) ) +* TEMP1 = BIG/DBLE(N) +* + CALL DLASCL( 'G', 0, 0, AAPP, TEMP1, N, 1, SVA, N, IERR ) + IF ( AAQQ .GT. (AAPP * SFMIN) ) THEN + AAQQ = ( AAQQ / AAPP ) * TEMP1 + ELSE + AAQQ = ( AAQQ * TEMP1 ) / AAPP + END IF + TEMP1 = TEMP1 * SCALEM + CALL ZLASCL( 'G', 0, 0, AAPP, TEMP1, M, N, A, LDA, IERR ) +* +* To undo scaling at the end of this procedure, multiply the +* computed singular values with USCAL2 / USCAL1. +* + USCAL1 = TEMP1 + USCAL2 = AAPP +* + IF ( L2KILL ) THEN +* L2KILL enforces computation of nonzero singular values in +* the restricted range of condition number of the initial A, +* sigma_max(A) / sigma_min(A) approx. SQRT(BIG)/SQRT(SFMIN). + XSC = SQRT( SFMIN ) + ELSE + XSC = SMALL +* +* Now, if the condition number of A is too big, +* sigma_max(A) / sigma_min(A) .GT. SQRT(BIG/N) * EPSLN / SFMIN, +* as a precaution measure, the full SVD is computed using ZGESVJ +* with accumulated Jacobi rotations. This provides numerically +* more robust computation, at the cost of slightly increased run +* time. Depending on the concrete implementation of BLAS and LAPACK +* (i.e. how they behave in presence of extreme ill-conditioning) the +* implementor may decide to remove this switch. + IF ( ( AAQQ.LT.SQRT(SFMIN) ) .AND. LSVEC .AND. RSVEC ) THEN + JRACC = .TRUE. + END IF +* + END IF + IF ( AAQQ .LT. XSC ) THEN + DO 700 p = 1, N + IF ( SVA(p) .LT. XSC ) THEN + CALL ZLASET( 'A', M, 1, CZERO, CZERO, A(1,p), LDA ) + SVA(p) = ZERO + END IF + 700 CONTINUE + END IF +* +* Preconditioning using QR factorization with pivoting +* + IF ( ROWPIV ) THEN +* Optional row permutation (Bjoerck row pivoting): +* A result by Cox and Higham shows that the Bjoerck's +* row pivoting combined with standard column pivoting +* has similar effect as Powell-Reid complete pivoting. +* The ell-infinity norms of A are made nonincreasing. + IF ( ( LSVEC .AND. RSVEC ) .AND. .NOT.( JRACC ) ) THEN + IWOFF = 2*N + ELSE + IWOFF = N + END IF + DO 1952 p = 1, M - 1 + q = IDAMAX( M-p+1, RWORK(M+p), 1 ) + p - 1 + IWORK(IWOFF+p) = q + IF ( p .NE. q ) THEN + TEMP1 = RWORK(M+p) + RWORK(M+p) = RWORK(M+q) + RWORK(M+q) = TEMP1 + END IF + 1952 CONTINUE + CALL ZLASWP( N, A, LDA, 1, M-1, IWORK(IWOFF+1), 1 ) + END IF +* +* End of the preparation phase (scaling, optional sorting and +* transposing, optional flushing of small columns). +* +* Preconditioning +* +* If the full SVD is needed, the right singular vectors are computed +* from a matrix equation, and for that we need theoretical analysis +* of the Businger-Golub pivoting. So we use ZGEQP3 as the first RR QRF. +* In all other cases the first RR QRF can be chosen by other criteria +* (eg speed by replacing global with restricted window pivoting, such +* as in xGEQPX from TOMS # 782). Good results will be obtained using +* xGEQPX with properly (!) chosen numerical parameters. +* Any improvement of ZGEQP3 improves overall performance of ZGEJSV. +* +* A * P1 = Q1 * [ R1^* 0]^*: + DO 1963 p = 1, N +* .. all columns are free columns + IWORK(p) = 0 + 1963 CONTINUE + CALL ZGEQP3( M, N, A, LDA, IWORK, CWORK, CWORK(N+1), LWORK-N, + $ RWORK, IERR ) +* +* The upper triangular matrix R1 from the first QRF is inspected for +* rank deficiency and possibilities for deflation, or possible +* ill-conditioning. Depending on the user specified flag L2RANK, +* the procedure explores possibilities to reduce the numerical +* rank by inspecting the computed upper triangular factor. If +* L2RANK or L2ABER are up, then ZGEJSV will compute the SVD of +* A + dA, where ||dA|| <= f(M,N)*EPSLN. +* + NR = 1 + IF ( L2ABER ) THEN +* Standard absolute error bound suffices. All sigma_i with +* sigma_i < N*EPSLN*||A|| are flushed to zero. This is an +* aggressive enforcement of lower numerical rank by introducing a +* backward error of the order of N*EPSLN*||A||. + TEMP1 = SQRT(DBLE(N))*EPSLN + DO 3001 p = 2, N + IF ( ABS(A(p,p)) .GE. (TEMP1*ABS(A(1,1))) ) THEN + NR = NR + 1 + ELSE + GO TO 3002 + END IF + 3001 CONTINUE + 3002 CONTINUE + ELSE IF ( L2RANK ) THEN +* .. similarly as above, only slightly more gentle (less aggressive). +* Sudden drop on the diagonal of R1 is used as the criterion for +* close-to-rank-deficient. + TEMP1 = SQRT(SFMIN) + DO 3401 p = 2, N + IF ( ( ABS(A(p,p)) .LT. (EPSLN*ABS(A(p-1,p-1))) ) .OR. + $ ( ABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (ABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3402 + NR = NR + 1 + 3401 CONTINUE + 3402 CONTINUE +* + ELSE +* The goal is high relative accuracy. However, if the matrix +* has high scaled condition number the relative accuracy is in +* general not feasible. Later on, a condition number estimator +* will be deployed to estimate the scaled condition number. +* Here we just remove the underflowed part of the triangular +* factor. This prevents the situation in which the code is +* working hard to get the accuracy not warranted by the data. + TEMP1 = SQRT(SFMIN) + DO 3301 p = 2, N + IF ( ( ABS(A(p,p)) .LT. SMALL ) .OR. + $ ( L2KILL .AND. (ABS(A(p,p)) .LT. TEMP1) ) ) GO TO 3302 + NR = NR + 1 + 3301 CONTINUE + 3302 CONTINUE +* + END IF +* + ALMORT = .FALSE. + IF ( NR .EQ. N ) THEN + MAXPRJ = ONE + DO 3051 p = 2, N + TEMP1 = ABS(A(p,p)) / SVA(IWORK(p)) + MAXPRJ = MIN( MAXPRJ, TEMP1 ) + 3051 CONTINUE + IF ( MAXPRJ**2 .GE. ONE - DBLE(N)*EPSLN ) ALMORT = .TRUE. + END IF +* +* + SCONDA = - ONE + CONDR1 = - ONE + CONDR2 = - ONE +* + IF ( ERREST ) THEN + IF ( N .EQ. NR ) THEN + IF ( RSVEC ) THEN +* .. V is available as workspace + CALL ZLACPY( 'U', N, N, A, LDA, V, LDV ) + DO 3053 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL ZDSCAL( p, ONE/TEMP1, V(1,p), 1 ) + 3053 CONTINUE + IF ( LSVEC )THEN + CALL ZPOCON( 'U', N, V, LDV, ONE, TEMP1, + $ CWORK(N+1), RWORK, IERR ) + ELSE + CALL ZPOCON( 'U', N, V, LDV, ONE, TEMP1, + $ CWORK, RWORK, IERR ) + END IF +* + ELSE IF ( LSVEC ) THEN +* .. U is available as workspace + CALL ZLACPY( 'U', N, N, A, LDA, U, LDU ) + DO 3054 p = 1, N + TEMP1 = SVA(IWORK(p)) + CALL ZDSCAL( p, ONE/TEMP1, U(1,p), 1 ) + 3054 CONTINUE + CALL ZPOCON( 'U', N, U, LDU, ONE, TEMP1, + $ CWORK(N+1), RWORK, IERR ) + ELSE + CALL ZLACPY( 'U', N, N, A, LDA, CWORK, N ) +*[] CALL ZLACPY( 'U', N, N, A, LDA, CWORK(N+1), N ) +* Change: here index shifted by N to the left, CWORK(1:N) +* not needed for SIGMA only computation + DO 3052 p = 1, N + TEMP1 = SVA(IWORK(p)) +*[] CALL ZDSCAL( p, ONE/TEMP1, CWORK(N+(p-1)*N+1), 1 ) + CALL ZDSCAL( p, ONE/TEMP1, CWORK((p-1)*N+1), 1 ) + 3052 CONTINUE +* .. the columns of R are scaled to have unit Euclidean lengths. +*[] CALL ZPOCON( 'U', N, CWORK(N+1), N, ONE, TEMP1, +*[] $ CWORK(N+N*N+1), RWORK, IERR ) + CALL ZPOCON( 'U', N, CWORK, N, ONE, TEMP1, + $ CWORK(N*N+1), RWORK, IERR ) +* + END IF + IF ( TEMP1 .NE. ZERO ) THEN + SCONDA = ONE / SQRT(TEMP1) + ELSE + SCONDA = - ONE + END IF +* SCONDA is an estimate of SQRT(||(R^* * R)^(-1)||_1). +* N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA + ELSE + SCONDA = - ONE + END IF + END IF +* + L2PERT = L2PERT .AND. ( ABS( A(1,1)/A(NR,NR) ) .GT. SQRT(BIG1) ) +* If there is no violent scaling, artificial perturbation is not needed. +* +* Phase 3: +* + IF ( .NOT. ( RSVEC .OR. LSVEC ) ) THEN +* +* Singular Values only +* +* .. transpose A(1:NR,1:N) + DO 1946 p = 1, MIN( N-1, NR ) + CALL ZCOPY( N-p, A(p,p+1), LDA, A(p+1,p), 1 ) + CALL ZLACGV( N-p+1, A(p,p), 1 ) + 1946 CONTINUE + IF ( NR .EQ. N ) A(N,N) = CONJG(A(N,N)) +* +* The following two DO-loops introduce small relative perturbation +* into the strict upper triangle of the lower triangular matrix. +* Small entries below the main diagonal are also changed. +* This modification is useful if the computing environment does not +* provide/allow FLUSH TO ZERO underflow, for it prevents many +* annoying denormalized numbers in case of strongly scaled matrices. +* The perturbation is structured so that it does not introduce any +* new perturbation of the singular values, and it does not destroy +* the job done by the preconditioner. +* The licence for this perturbation is in the variable L2PERT, which +* should be .FALSE. if FLUSH TO ZERO underflow is active. +* + IF ( .NOT. ALMORT ) THEN +* + IF ( L2PERT ) THEN +* XSC = SQRT(SMALL) + XSC = EPSLN / DBLE(N) + DO 4947 q = 1, NR + CTEMP = DCMPLX(XSC*ABS(A(q,q)),ZERO) + DO 4949 p = 1, N + IF ( ( (p.GT.q) .AND. (ABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) +* $ A(p,q) = TEMP1 * ( A(p,q) / ABS(A(p,q)) ) + $ A(p,q) = CTEMP + 4949 CONTINUE + 4947 CONTINUE + ELSE + CALL ZLASET( 'U', NR-1,NR-1, CZERO,CZERO, A(1,2),LDA ) + END IF +* +* .. second preconditioning using the QR factorization +* + CALL ZGEQRF( N,NR, A,LDA, CWORK, CWORK(N+1),LWORK-N, IERR ) +* +* .. and transpose upper to lower triangular + DO 1948 p = 1, NR - 1 + CALL ZCOPY( NR-p, A(p,p+1), LDA, A(p+1,p), 1 ) + CALL ZLACGV( NR-p+1, A(p,p), 1 ) + 1948 CONTINUE +* + END IF +* +* Row-cyclic Jacobi SVD algorithm with column pivoting +* +* .. again some perturbation (a "background noise") is added +* to drown denormals + IF ( L2PERT ) THEN +* XSC = SQRT(SMALL) + XSC = EPSLN / DBLE(N) + DO 1947 q = 1, NR + CTEMP = DCMPLX(XSC*ABS(A(q,q)),ZERO) + DO 1949 p = 1, NR + IF ( ( (p.GT.q) .AND. (ABS(A(p,q)).LE.TEMP1) ) + $ .OR. ( p .LT. q ) ) +* $ A(p,q) = TEMP1 * ( A(p,q) / ABS(A(p,q)) ) + $ A(p,q) = CTEMP + 1949 CONTINUE + 1947 CONTINUE + ELSE + CALL ZLASET( 'U', NR-1, NR-1, CZERO, CZERO, A(1,2), LDA ) + END IF +* +* .. and one-sided Jacobi rotations are started on a lower +* triangular matrix (plus perturbation which is ignored in +* the part which destroys triangular form (confusing?!)) +* + CALL ZGESVJ( 'L', 'N', 'N', NR, NR, A, LDA, SVA, + $ N, V, LDV, CWORK, LWORK, RWORK, LRWORK, INFO ) +* + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) +* +* + ELSE IF ( ( RSVEC .AND. ( .NOT. LSVEC ) .AND. ( .NOT. JRACC ) ) + $ .OR. + $ ( JRACC .AND. ( .NOT. LSVEC ) .AND. ( NR .NE. N ) ) ) THEN +* +* -> Singular Values and Right Singular Vectors <- +* + IF ( ALMORT ) THEN +* +* .. in this case NR equals N + DO 1998 p = 1, NR + CALL ZCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL ZLACGV( N-p+1, V(p,p), 1 ) + 1998 CONTINUE + CALL ZLASET( 'U', NR-1,NR-1, CZERO, CZERO, V(1,2), LDV ) +* + CALL ZGESVJ( 'L','U','N', N, NR, V, LDV, SVA, NR, A, LDA, + $ CWORK, LWORK, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + + ELSE +* +* .. two more QR factorizations ( one QRF is not enough, two require +* accumulated product of Jacobi rotations, three are perfect ) +* + CALL ZLASET( 'L', NR-1,NR-1, CZERO, CZERO, A(2,1), LDA ) + CALL ZGELQF( NR,N, A, LDA, CWORK, CWORK(N+1), LWORK-N, IERR) + CALL ZLACPY( 'L', NR, NR, A, LDA, V, LDV ) + CALL ZLASET( 'U', NR-1,NR-1, CZERO, CZERO, V(1,2), LDV ) + CALL ZGEQRF( NR, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) + DO 8998 p = 1, NR + CALL ZCOPY( NR-p+1, V(p,p), LDV, V(p,p), 1 ) + CALL ZLACGV( NR-p+1, V(p,p), 1 ) + 8998 CONTINUE + CALL ZLASET('U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV) +* + CALL ZGESVJ( 'L', 'U','N', NR, NR, V,LDV, SVA, NR, U, + $ LDU, CWORK(N+1), LWORK-N, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + IF ( NR .LT. N ) THEN + CALL ZLASET( 'A',N-NR, NR, CZERO,CZERO, V(NR+1,1), LDV ) + CALL ZLASET( 'A',NR, N-NR, CZERO,CZERO, V(1,NR+1), LDV ) + CALL ZLASET( 'A',N-NR,N-NR,CZERO,CONE, V(NR+1,NR+1),LDV ) + END IF +* + CALL ZUNMLQ( 'L', 'C', N, N, NR, A, LDA, CWORK, + $ V, LDV, CWORK(N+1), LWORK-N, IERR ) +* + END IF +* .. permute the rows of V +* DO 8991 p = 1, N +* CALL ZCOPY( N, V(p,1), LDV, A(IWORK(p),1), LDA ) +* 8991 CONTINUE +* CALL ZLACPY( 'All', N, N, A, LDA, V, LDV ) + CALL ZLAPMR( .FALSE., N, N, V, LDV, IWORK ) +* + IF ( TRANSP ) THEN + CALL ZLACPY( 'A', N, N, V, LDV, U, LDU ) + END IF +* + ELSE IF ( JRACC .AND. (.NOT. LSVEC) .AND. ( NR.EQ. N ) ) THEN +* + CALL ZLASET( 'L', N-1,N-1, CZERO, CZERO, A(2,1), LDA ) +* + CALL ZGESVJ( 'U','N','V', N, N, A, LDA, SVA, N, V, LDV, + $ CWORK, LWORK, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + CALL ZLAPMR( .FALSE., N, N, V, LDV, IWORK ) +* + ELSE IF ( LSVEC .AND. ( .NOT. RSVEC ) ) THEN +* +* .. Singular Values and Left Singular Vectors .. +* +* .. second preconditioning step to avoid need to accumulate +* Jacobi rotations in the Jacobi iterations. + DO 1965 p = 1, NR + CALL ZCOPY( N-p+1, A(p,p), LDA, U(p,p), 1 ) + CALL ZLACGV( N-p+1, U(p,p), 1 ) + 1965 CONTINUE + CALL ZLASET( 'U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) +* + CALL ZGEQRF( N, NR, U, LDU, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) +* + DO 1967 p = 1, NR - 1 + CALL ZCOPY( NR-p, U(p,p+1), LDU, U(p+1,p), 1 ) + CALL ZLACGV( N-p+1, U(p,p), 1 ) + 1967 CONTINUE + CALL ZLASET( 'U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) +* + CALL ZGESVJ( 'L', 'U', 'N', NR,NR, U, LDU, SVA, NR, A, + $ LDA, CWORK(N+1), LWORK-N, RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) +* + IF ( NR .LT. M ) THEN + CALL ZLASET( 'A', M-NR, NR,CZERO, CZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL ZLASET( 'A',NR, N1-NR, CZERO, CZERO, U(1,NR+1),LDU ) + CALL ZLASET( 'A',M-NR,N1-NR,CZERO,CONE,U(NR+1,NR+1),LDU ) + END IF + END IF +* + CALL ZUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL ZLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + DO 1974 p = 1, N1 + XSC = ONE / DZNRM2( M, U(1,p), 1 ) + CALL ZDSCAL( M, XSC, U(1,p), 1 ) + 1974 CONTINUE +* + IF ( TRANSP ) THEN + CALL ZLACPY( 'A', N, N, U, LDU, V, LDV ) + END IF +* + ELSE +* +* .. Full SVD .. +* + IF ( .NOT. JRACC ) THEN +* + IF ( .NOT. ALMORT ) THEN +* +* Second Preconditioning Step (QRF [with pivoting]) +* Note that the composition of TRANSPOSE, QRF and TRANSPOSE is +* equivalent to an LQF CALL. Since in many libraries the QRF +* seems to be better optimized than the LQF, we do explicit +* transpose and use the QRF. This is subject to changes in an +* optimized implementation of ZGEJSV. +* + DO 1968 p = 1, NR + CALL ZCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL ZLACGV( N-p+1, V(p,p), 1 ) + 1968 CONTINUE +* +* .. the following two loops perturb small entries to avoid +* denormals in the second QR factorization, where they are +* as good as zeros. This is done to avoid painfully slow +* computation with denormals. The relative size of the perturbation +* is a parameter that can be changed by the implementer. +* This perturbation device will be obsolete on machines with +* properly implemented arithmetic. +* To switch it off, set L2PERT=.FALSE. To remove it from the +* code, remove the action under L2PERT=.TRUE., leave the ELSE part. +* The following two loops should be blocked and fused with the +* transposed copy above. +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 2969 q = 1, NR + CTEMP = DCMPLX(XSC*ABS( V(q,q) ),ZERO) + DO 2968 p = 1, N + IF ( ( p .GT. q ) .AND. ( ABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) +* $ V(p,q) = TEMP1 * ( V(p,q) / ABS(V(p,q)) ) + $ V(p,q) = CTEMP + IF ( p .LT. q ) V(p,q) = - V(p,q) + 2968 CONTINUE + 2969 CONTINUE + ELSE + CALL ZLASET( 'U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV ) + END IF +* +* Estimate the row scaled condition number of R1 +* (If R1 is rectangular, N > NR, then the condition number +* of the leading NR x NR submatrix is estimated.) +* + CALL ZLACPY( 'L', NR, NR, V, LDV, CWORK(2*N+1), NR ) + DO 3950 p = 1, NR + TEMP1 = DZNRM2(NR-p+1,CWORK(2*N+(p-1)*NR+p),1) + CALL ZDSCAL(NR-p+1,ONE/TEMP1,CWORK(2*N+(p-1)*NR+p),1) + 3950 CONTINUE + CALL ZPOCON('L',NR,CWORK(2*N+1),NR,ONE,TEMP1, + $ CWORK(2*N+NR*NR+1),RWORK,IERR) + CONDR1 = ONE / SQRT(TEMP1) +* .. here need a second opinion on the condition number +* .. then assume worst case scenario +* R1 is OK for inverse <=> CONDR1 .LT. DBLE(N) +* more conservative <=> CONDR1 .LT. SQRT(DBLE(N)) +* + COND_OK = SQRT(SQRT(DBLE(NR))) +*[TP] COND_OK is a tuning parameter. +* + IF ( CONDR1 .LT. COND_OK ) THEN +* .. the second QRF without pivoting. Note: in an optimized +* implementation, this QRF should be implemented as the QRF +* of a lower triangular matrix. +* R1^* = Q2 * R2 + CALL ZGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL)/EPSLN + DO 3959 p = 2, NR + DO 3958 q = 1, p - 1 + CTEMP=DCMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) + IF ( ABS(V(q,p)) .LE. TEMP1 ) +* $ V(q,p) = TEMP1 * ( V(q,p) / ABS(V(q,p)) ) + $ V(q,p) = CTEMP + 3958 CONTINUE + 3959 CONTINUE + END IF +* + IF ( NR .NE. N ) + $ CALL ZLACPY( 'A', N, NR, V, LDV, CWORK(2*N+1), N ) +* .. save ... +* +* .. this transposed copy should be better than naive + DO 1969 p = 1, NR - 1 + CALL ZCOPY( NR-p, V(p,p+1), LDV, V(p+1,p), 1 ) + CALL ZLACGV(NR-p+1, V(p,p), 1 ) + 1969 CONTINUE + V(NR,NR)=CONJG(V(NR,NR)) +* + CONDR2 = CONDR1 +* + ELSE +* +* .. ill-conditioned case: second QRF with pivoting +* Note that windowed pivoting would be equally good +* numerically, and more run-time efficient. So, in +* an optimal implementation, the next call to ZGEQP3 +* should be replaced with eg. CALL ZGEQPX (ACM TOMS #782) +* with properly (carefully) chosen parameters. +* +* R1^* * P2 = Q2 * R2 + DO 3003 p = 1, NR + IWORK(N+p) = 0 + 3003 CONTINUE + CALL ZGEQP3( N, NR, V, LDV, IWORK(N+1), CWORK(N+1), + $ CWORK(2*N+1), LWORK-2*N, RWORK, IERR ) +** CALL ZGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), +** $ LWORK-2*N, IERR ) + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 3969 p = 2, NR + DO 3968 q = 1, p - 1 + CTEMP=DCMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) + IF ( ABS(V(q,p)) .LE. TEMP1 ) +* $ V(q,p) = TEMP1 * ( V(q,p) / ABS(V(q,p)) ) + $ V(q,p) = CTEMP + 3968 CONTINUE + 3969 CONTINUE + END IF +* + CALL ZLACPY( 'A', N, NR, V, LDV, CWORK(2*N+1), N ) +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 8970 p = 2, NR + DO 8971 q = 1, p - 1 + CTEMP=DCMPLX(XSC*MIN(ABS(V(p,p)),ABS(V(q,q))), + $ ZERO) +* V(p,q) = - TEMP1*( V(q,p) / ABS(V(q,p)) ) + V(p,q) = - CTEMP + 8971 CONTINUE + 8970 CONTINUE + ELSE + CALL ZLASET( 'L',NR-1,NR-1,CZERO,CZERO,V(2,1),LDV ) + END IF +* Now, compute R2 = L3 * Q3, the LQ factorization. + CALL ZGELQF( NR, NR, V, LDV, CWORK(2*N+N*NR+1), + $ CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, IERR ) +* .. and estimate the condition number + CALL ZLACPY( 'L',NR,NR,V,LDV,CWORK(2*N+N*NR+NR+1),NR ) + DO 4950 p = 1, NR + TEMP1 = DZNRM2( p, CWORK(2*N+N*NR+NR+p), NR ) + CALL ZDSCAL( p, ONE/TEMP1, CWORK(2*N+N*NR+NR+p), NR ) + 4950 CONTINUE + CALL ZPOCON( 'L',NR,CWORK(2*N+N*NR+NR+1),NR,ONE,TEMP1, + $ CWORK(2*N+N*NR+NR+NR*NR+1),RWORK,IERR ) + CONDR2 = ONE / SQRT(TEMP1) +* +* + IF ( CONDR2 .GE. COND_OK ) THEN +* .. save the Householder vectors used for Q3 +* (this overwrites the copy of R2, as it will not be +* needed in this branch, but it does not overwritte the +* Huseholder vectors of Q2.). + CALL ZLACPY( 'U', NR, NR, V, LDV, CWORK(2*N+1), N ) +* .. and the rest of the information on Q3 is in +* WORK(2*N+N*NR+1:2*N+N*NR+N) + END IF +* + END IF +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 4968 q = 2, NR + CTEMP = XSC * V(q,q) + DO 4969 p = 1, q - 1 +* V(p,q) = - TEMP1*( V(p,q) / ABS(V(p,q)) ) + V(p,q) = - CTEMP + 4969 CONTINUE + 4968 CONTINUE + ELSE + CALL ZLASET( 'U', NR-1,NR-1, CZERO,CZERO, V(1,2), LDV ) + END IF +* +* Second preconditioning finished; continue with Jacobi SVD +* The input matrix is lower trinagular. +* +* Recover the right singular vectors as solution of a well +* conditioned triangular matrix equation. +* + IF ( CONDR1 .LT. COND_OK ) THEN +* + CALL ZGESVJ( 'L','U','N',NR,NR,V,LDV,SVA,NR,U, LDU, + $ CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,RWORK, + $ LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 3970 p = 1, NR + CALL ZCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL ZDSCAL( NR, SVA(p), V(1,p), 1 ) + 3970 CONTINUE + +* .. pick the right matrix equation and solve it +* + IF ( NR .EQ. N ) THEN +* :)) .. best case, R1 is inverted. The solution of this matrix +* equation is Q2*V2 = the product of the Jacobi rotations +* used in ZGESVJ, premultiplied with the orthogonal matrix +* from the second QR factorization. + CALL ZTRSM('L','U','N','N', NR,NR,CONE, A,LDA, V,LDV) + ELSE +* .. R1 is well conditioned, but non-square. Adjoint of R2 +* is inverted to get the product of the Jacobi rotations +* used in ZGESVJ. The Q-factor from the second QR +* factorization is then built in explicitly. + CALL ZTRSM('L','U','C','N',NR,NR,CONE,CWORK(2*N+1), + $ N,V,LDV) + IF ( NR .LT. N ) THEN + CALL ZLASET('A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV) + CALL ZLASET('A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV) + CALL ZLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL ZUNMQR('L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR) + END IF +* + ELSE IF ( CONDR2 .LT. COND_OK ) THEN +* +* The matrix R2 is inverted. The solution of the matrix equation +* is Q3^* * V3 = the product of the Jacobi rotations (appplied to +* the lower triangular L3 from the LQ factorization of +* R2=L3*Q3), pre-multiplied with the transposed Q3. + CALL ZGESVJ( 'L', 'U', 'N', NR, NR, V, LDV, SVA, NR, U, + $ LDU, CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 3870 p = 1, NR + CALL ZCOPY( NR, V(1,p), 1, U(1,p), 1 ) + CALL ZDSCAL( NR, SVA(p), U(1,p), 1 ) + 3870 CONTINUE + CALL ZTRSM('L','U','N','N',NR,NR,CONE,CWORK(2*N+1),N, + $ U,LDU) +* .. apply the permutation from the second QR factorization + DO 873 q = 1, NR + DO 872 p = 1, NR + CWORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 872 CONTINUE + DO 874 p = 1, NR + U(p,q) = CWORK(2*N+N*NR+NR+p) + 874 CONTINUE + 873 CONTINUE + IF ( NR .LT. N ) THEN + CALL ZLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL ZLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL ZLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL ZUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) + ELSE +* Last line of defense. +* #:( This is a rather pathological case: no scaled condition +* improvement after two pivoted QR factorizations. Other +* possibility is that the rank revealing QR factorization +* or the condition estimator has failed, or the COND_OK +* is set very close to ONE (which is unnecessary). Normally, +* this branch should never be executed, but in rare cases of +* failure of the RRQR or condition estimator, the last line of +* defense ensures that ZGEJSV completes the task. +* Compute the full SVD of L3 using ZGESVJ with explicit +* accumulation of Jacobi rotations. + CALL ZGESVJ( 'L', 'U', 'V', NR, NR, V, LDV, SVA, NR, U, + $ LDU, CWORK(2*N+N*NR+NR+1), LWORK-2*N-N*NR-NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + IF ( NR .LT. N ) THEN + CALL ZLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL ZLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL ZLASET('A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV) + END IF + CALL ZUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* + CALL ZUNMLQ( 'L', 'C', NR, NR, NR, CWORK(2*N+1), N, + $ CWORK(2*N+N*NR+1), U, LDU, CWORK(2*N+N*NR+NR+1), + $ LWORK-2*N-N*NR-NR, IERR ) + DO 773 q = 1, NR + DO 772 p = 1, NR + CWORK(2*N+N*NR+NR+IWORK(N+p)) = U(p,q) + 772 CONTINUE + DO 774 p = 1, NR + U(p,q) = CWORK(2*N+N*NR+NR+p) + 774 CONTINUE + 773 CONTINUE +* + END IF +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = SQRT(DBLE(N)) * EPSLN + DO 1972 q = 1, N + DO 972 p = 1, N + CWORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 972 CONTINUE + DO 973 p = 1, N + V(p,q) = CWORK(2*N+N*NR+NR+p) + 973 CONTINUE + XSC = ONE / DZNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL ZDSCAL( N, XSC, V(1,q), 1 ) + 1972 CONTINUE +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). + IF ( NR .LT. M ) THEN + CALL ZLASET('A', M-NR, NR, CZERO, CZERO, U(NR+1,1), LDU) + IF ( NR .LT. N1 ) THEN + CALL ZLASET('A',NR,N1-NR,CZERO,CZERO,U(1,NR+1),LDU) + CALL ZLASET('A',M-NR,N1-NR,CZERO,CONE, + $ U(NR+1,NR+1),LDU) + END IF + END IF +* +* The Q matrix from the first QRF is built into the left singular +* matrix U. This applies to all cases. +* + CALL ZUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) + +* The columns of U are normalized. The cost is O(M*N) flops. + TEMP1 = SQRT(DBLE(M)) * EPSLN + DO 1973 p = 1, NR + XSC = ONE / DZNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL ZDSCAL( M, XSC, U(1,p), 1 ) + 1973 CONTINUE +* +* If the initial QRF is computed with row pivoting, the left +* singular vectors must be adjusted. +* + IF ( ROWPIV ) + $ CALL ZLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + ELSE +* +* .. the initial matrix A has almost orthogonal columns and +* the second QRF is not needed +* + CALL ZLACPY( 'U', N, N, A, LDA, CWORK(N+1), N ) + IF ( L2PERT ) THEN + XSC = SQRT(SMALL) + DO 5970 p = 2, N + CTEMP = XSC * CWORK( N + (p-1)*N + p ) + DO 5971 q = 1, p - 1 +* CWORK(N+(q-1)*N+p)=-TEMP1 * ( CWORK(N+(p-1)*N+q) / +* $ ABS(CWORK(N+(p-1)*N+q)) ) + CWORK(N+(q-1)*N+p)=-CTEMP + 5971 CONTINUE + 5970 CONTINUE + ELSE + CALL ZLASET( 'L',N-1,N-1,CZERO,CZERO,CWORK(N+2),N ) + END IF +* + CALL ZGESVJ( 'U', 'U', 'N', N, N, CWORK(N+1), N, SVA, + $ N, U, LDU, CWORK(N+N*N+1), LWORK-N-N*N, RWORK, LRWORK, + $ INFO ) +* + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + DO 6970 p = 1, N + CALL ZCOPY( N, CWORK(N+(p-1)*N+1), 1, U(1,p), 1 ) + CALL ZDSCAL( N, SVA(p), CWORK(N+(p-1)*N+1), 1 ) + 6970 CONTINUE +* + CALL ZTRSM( 'L', 'U', 'N', 'N', N, N, + $ CONE, A, LDA, CWORK(N+1), N ) + DO 6972 p = 1, N + CALL ZCOPY( N, CWORK(N+p), N, V(IWORK(p),1), LDV ) + 6972 CONTINUE + TEMP1 = SQRT(DBLE(N))*EPSLN + DO 6971 p = 1, N + XSC = ONE / DZNRM2( N, V(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL ZDSCAL( N, XSC, V(1,p), 1 ) + 6971 CONTINUE +* +* Assemble the left singular vector matrix U (M x N). +* + IF ( N .LT. M ) THEN + CALL ZLASET( 'A', M-N, N, CZERO, CZERO, U(N+1,1), LDU ) + IF ( N .LT. N1 ) THEN + CALL ZLASET('A',N, N1-N, CZERO, CZERO, U(1,N+1),LDU) + CALL ZLASET( 'A',M-N,N1-N, CZERO, CONE,U(N+1,N+1),LDU) + END IF + END IF + CALL ZUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) + TEMP1 = SQRT(DBLE(M))*EPSLN + DO 6973 p = 1, N1 + XSC = ONE / DZNRM2( M, U(1,p), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL ZDSCAL( M, XSC, U(1,p), 1 ) + 6973 CONTINUE +* + IF ( ROWPIV ) + $ CALL ZLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* + END IF +* +* end of the >> almost orthogonal case << in the full SVD +* + ELSE +* +* This branch deploys a preconditioned Jacobi SVD with explicitly +* accumulated rotations. It is included as optional, mainly for +* experimental purposes. It does perform well, and can also be used. +* In this implementation, this branch will be automatically activated +* if the condition number sigma_max(A) / sigma_min(A) is predicted +* to be greater than the overflow threshold. This is because the +* a posteriori computation of the singular vectors assumes robust +* implementation of BLAS and some LAPACK procedures, capable of working +* in presence of extreme values, e.g. when the singular values spread from +* the underflow to the overflow threshold. +* + DO 7968 p = 1, NR + CALL ZCOPY( N-p+1, A(p,p), LDA, V(p,p), 1 ) + CALL ZLACGV( N-p+1, V(p,p), 1 ) + 7968 CONTINUE +* + IF ( L2PERT ) THEN + XSC = SQRT(SMALL/EPSLN) + DO 5969 q = 1, NR + CTEMP = DCMPLX(XSC*ABS( V(q,q) ),ZERO) + DO 5968 p = 1, N + IF ( ( p .GT. q ) .AND. ( ABS(V(p,q)) .LE. TEMP1 ) + $ .OR. ( p .LT. q ) ) +* $ V(p,q) = TEMP1 * ( V(p,q) / ABS(V(p,q)) ) + $ V(p,q) = CTEMP + IF ( p .LT. q ) V(p,q) = - V(p,q) + 5968 CONTINUE + 5969 CONTINUE + ELSE + CALL ZLASET( 'U', NR-1, NR-1, CZERO, CZERO, V(1,2), LDV ) + END IF + + CALL ZGEQRF( N, NR, V, LDV, CWORK(N+1), CWORK(2*N+1), + $ LWORK-2*N, IERR ) + CALL ZLACPY( 'L', N, NR, V, LDV, CWORK(2*N+1), N ) +* + DO 7969 p = 1, NR + CALL ZCOPY( NR-p+1, V(p,p), LDV, U(p,p), 1 ) + CALL ZLACGV( NR-p+1, U(p,p), 1 ) + 7969 CONTINUE + + IF ( L2PERT ) THEN + XSC = SQRT(SMALL/EPSLN) + DO 9970 q = 2, NR + DO 9971 p = 1, q - 1 + CTEMP = DCMPLX(XSC * MIN(ABS(U(p,p)),ABS(U(q,q))), + $ ZERO) +* U(p,q) = - TEMP1 * ( U(q,p) / ABS(U(q,p)) ) + U(p,q) = - CTEMP + 9971 CONTINUE + 9970 CONTINUE + ELSE + CALL ZLASET('U', NR-1, NR-1, CZERO, CZERO, U(1,2), LDU ) + END IF + + CALL ZGESVJ( 'L', 'U', 'V', NR, NR, U, LDU, SVA, + $ N, V, LDV, CWORK(2*N+N*NR+1), LWORK-2*N-N*NR, + $ RWORK, LRWORK, INFO ) + SCALEM = RWORK(1) + NUMRANK = NINT(RWORK(2)) + + IF ( NR .LT. N ) THEN + CALL ZLASET( 'A',N-NR,NR,CZERO,CZERO,V(NR+1,1),LDV ) + CALL ZLASET( 'A',NR,N-NR,CZERO,CZERO,V(1,NR+1),LDV ) + CALL ZLASET( 'A',N-NR,N-NR,CZERO,CONE,V(NR+1,NR+1),LDV ) + END IF + + CALL ZUNMQR( 'L','N',N,N,NR,CWORK(2*N+1),N,CWORK(N+1), + $ V,LDV,CWORK(2*N+N*NR+NR+1),LWORK-2*N-N*NR-NR,IERR ) +* +* Permute the rows of V using the (column) permutation from the +* first QRF. Also, scale the columns to make them unit in +* Euclidean norm. This applies to all cases. +* + TEMP1 = SQRT(DBLE(N)) * EPSLN + DO 7972 q = 1, N + DO 8972 p = 1, N + CWORK(2*N+N*NR+NR+IWORK(p)) = V(p,q) + 8972 CONTINUE + DO 8973 p = 1, N + V(p,q) = CWORK(2*N+N*NR+NR+p) + 8973 CONTINUE + XSC = ONE / DZNRM2( N, V(1,q), 1 ) + IF ( (XSC .LT. (ONE-TEMP1)) .OR. (XSC .GT. (ONE+TEMP1)) ) + $ CALL ZDSCAL( N, XSC, V(1,q), 1 ) + 7972 CONTINUE +* +* At this moment, V contains the right singular vectors of A. +* Next, assemble the left singular vector matrix U (M x N). +* + IF ( NR .LT. M ) THEN + CALL ZLASET( 'A', M-NR, NR, CZERO, CZERO, U(NR+1,1), LDU ) + IF ( NR .LT. N1 ) THEN + CALL ZLASET('A',NR, N1-NR, CZERO, CZERO, U(1,NR+1),LDU) + CALL ZLASET('A',M-NR,N1-NR, CZERO, CONE,U(NR+1,NR+1),LDU) + END IF + END IF +* + CALL ZUNMQR( 'L', 'N', M, N1, N, A, LDA, CWORK, U, + $ LDU, CWORK(N+1), LWORK-N, IERR ) +* + IF ( ROWPIV ) + $ CALL ZLASWP( N1, U, LDU, 1, M-1, IWORK(IWOFF+1), -1 ) +* +* + END IF + IF ( TRANSP ) THEN +* .. swap U and V because the procedure worked on A^* + DO 6974 p = 1, N + CALL ZSWAP( N, U(1,p), 1, V(1,p), 1 ) + 6974 CONTINUE + END IF +* + END IF +* end of the full SVD +* +* Undo scaling, if necessary (and possible) +* + IF ( USCAL2 .LE. (BIG/SVA(1))*USCAL1 ) THEN + CALL DLASCL( 'G', 0, 0, USCAL1, USCAL2, NR, 1, SVA, N, IERR ) + USCAL1 = ONE + USCAL2 = ONE + END IF +* + IF ( NR .LT. N ) THEN + DO 3004 p = NR+1, N + SVA(p) = ZERO + 3004 CONTINUE + END IF +* + RWORK(1) = USCAL2 * SCALEM + RWORK(2) = USCAL1 + IF ( ERREST ) RWORK(3) = SCONDA + IF ( LSVEC .AND. RSVEC ) THEN + RWORK(4) = CONDR1 + RWORK(5) = CONDR2 + END IF + IF ( L2TRAN ) THEN + RWORK(6) = ENTRA + RWORK(7) = ENTRAT + END IF +* + IWORK(1) = NR + IWORK(2) = NUMRANK + IWORK(3) = WARNING + IF ( TRANSP ) THEN + IWORK(4) = 1 + ELSE + IWORK(4) = -1 + END IF + +* + RETURN +* .. +* .. END OF ZGEJSV +* .. + END +* diff --git a/lapack-netlib/zgesvx.f b/lapack-netlib/zgesvx.f new file mode 100644 index 000000000..3b193a1b2 --- /dev/null +++ b/lapack-netlib/zgesvx.f @@ -0,0 +1,602 @@ +*> \brief ZGESVX computes the solution to system of linear equations A * X = B for GE matrices +* +* =========== DOCUMENTATION =========== +* +* Online html documentation available at +* http://www.netlib.org/lapack/explore-html/ +* +*> \htmlonly +*> Download ZGESVX + dependencies +*> +*> [TGZ] +*> +*> [ZIP] +*> +*> [TXT] +*> \endhtmlonly +* +* Definition: +* =========== +* +* SUBROUTINE ZGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, +* EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, +* WORK, RWORK, INFO ) +* +* .. Scalar Arguments .. +* CHARACTER EQUED, FACT, TRANS +* INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS +* DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. +* INTEGER IPIV( * ) +* DOUBLE PRECISION BERR( * ), C( * ), FERR( * ), R( * ), +* $ RWORK( * ) +* COMPLEX*16 A( LDA, * ), AF( LDAF, * ), B( LDB, * ), +* $ WORK( * ), X( LDX, * ) +* .. +* +* +*> \par Purpose: +* ============= +*> +*> \verbatim +*> +*> ZGESVX uses the LU factorization to compute the solution to a complex +*> system of linear equations +*> A * X = B, +*> where A is an N-by-N matrix and X and B are N-by-NRHS matrices. +*> +*> Error bounds on the solution and a condition estimate are also +*> provided. +*> \endverbatim +* +*> \par Description: +* ================= +*> +*> \verbatim +*> +*> The following steps are performed: +*> +*> 1. If FACT = 'E', real scaling factors are computed to equilibrate +*> the system: +*> TRANS = 'N': diag(R)*A*diag(C) *inv(diag(C))*X = diag(R)*B +*> TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B +*> TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B +*> Whether or not the system will be equilibrated depends on the +*> scaling of the matrix A, but if equilibration is used, A is +*> overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') +*> or diag(C)*B (if TRANS = 'T' or 'C'). +*> +*> 2. If FACT = 'N' or 'E', the LU decomposition is used to factor the +*> matrix A (after equilibration if FACT = 'E') as +*> A = P * L * U, +*> where P is a permutation matrix, L is a unit lower triangular +*> matrix, and U is upper triangular. +*> +*> 3. If some U(i,i)=0, so that U is exactly singular, then the routine +*> returns with INFO = i. Otherwise, the factored form of A is used +*> to estimate the condition number of the matrix A. If the +*> reciprocal of the condition number is less than machine precision, +*> INFO = N+1 is returned as a warning, but the routine still goes on +*> to solve for X and compute error bounds as described below. +*> +*> 4. The system of equations is solved for X using the factored form +*> of A. +*> +*> 5. Iterative refinement is applied to improve the computed solution +*> matrix and calculate error bounds and backward error estimates +*> for it. +*> +*> 6. If equilibration was used, the matrix X is premultiplied by +*> diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so +*> that it solves the original system before equilibration. +*> \endverbatim +* +* Arguments: +* ========== +* +*> \param[in] FACT +*> \verbatim +*> FACT is CHARACTER*1 +*> Specifies whether or not the factored form of the matrix A is +*> supplied on entry, and if not, whether the matrix A should be +*> equilibrated before it is factored. +*> = 'F': On entry, AF and IPIV contain the factored form of A. +*> If EQUED is not 'N', the matrix A has been +*> equilibrated with scaling factors given by R and C. +*> A, AF, and IPIV are not modified. +*> = 'N': The matrix A will be copied to AF and factored. +*> = 'E': The matrix A will be equilibrated if necessary, then +*> copied to AF and factored. +*> \endverbatim +*> +*> \param[in] TRANS +*> \verbatim +*> TRANS is CHARACTER*1 +*> Specifies the form of the system of equations: +*> = 'N': A * X = B (No transpose) +*> = 'T': A**T * X = B (Transpose) +*> = 'C': A**H * X = B (Conjugate transpose) +*> \endverbatim +*> +*> \param[in] N +*> \verbatim +*> N is INTEGER +*> The number of linear equations, i.e., the order of the +*> matrix A. N >= 0. +*> \endverbatim +*> +*> \param[in] NRHS +*> \verbatim +*> NRHS is INTEGER +*> The number of right hand sides, i.e., the number of columns +*> of the matrices B and X. NRHS >= 0. +*> \endverbatim +*> +*> \param[in,out] A +*> \verbatim +*> A is COMPLEX*16 array, dimension (LDA,N) +*> On entry, the N-by-N matrix A. If FACT = 'F' and EQUED is +*> not 'N', then A must have been equilibrated by the scaling +*> factors in R and/or C. A is not modified if FACT = 'F' or +*> 'N', or if FACT = 'E' and EQUED = 'N' on exit. +*> +*> On exit, if EQUED .ne. 'N', A is scaled as follows: +*> EQUED = 'R': A := diag(R) * A +*> EQUED = 'C': A := A * diag(C) +*> EQUED = 'B': A := diag(R) * A * diag(C). +*> \endverbatim +*> +*> \param[in] LDA +*> \verbatim +*> LDA is INTEGER +*> The leading dimension of the array A. LDA >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] AF +*> \verbatim +*> AF is COMPLEX*16 array, dimension (LDAF,N) +*> If FACT = 'F', then AF is an input argument and on entry +*> contains the factors L and U from the factorization +*> A = P*L*U as computed by ZGETRF. If EQUED .ne. 'N', then +*> AF is the factored form of the equilibrated matrix A. +*> +*> If FACT = 'N', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then AF is an output argument and on exit +*> returns the factors L and U from the factorization A = P*L*U +*> of the equilibrated matrix A (see the description of A for +*> the form of the equilibrated matrix). +*> \endverbatim +*> +*> \param[in] LDAF +*> \verbatim +*> LDAF is INTEGER +*> The leading dimension of the array AF. LDAF >= max(1,N). +*> \endverbatim +*> +*> \param[in,out] IPIV +*> \verbatim +*> IPIV is INTEGER array, dimension (N) +*> If FACT = 'F', then IPIV is an input argument and on entry +*> contains the pivot indices from the factorization A = P*L*U +*> as computed by ZGETRF; row i of the matrix was interchanged +*> with row IPIV(i). +*> +*> If FACT = 'N', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the original matrix A. +*> +*> If FACT = 'E', then IPIV is an output argument and on exit +*> contains the pivot indices from the factorization A = P*L*U +*> of the equilibrated matrix A. +*> \endverbatim +*> +*> \param[in,out] EQUED +*> \verbatim +*> EQUED is CHARACTER*1 +*> Specifies the form of equilibration that was done. +*> = 'N': No equilibration (always true if FACT = 'N'). +*> = 'R': Row equilibration, i.e., A has been premultiplied by +*> diag(R). +*> = 'C': Column equilibration, i.e., A has been postmultiplied +*> by diag(C). +*> = 'B': Both row and column equilibration, i.e., A has been +*> replaced by diag(R) * A * diag(C). +*> EQUED is an input argument if FACT = 'F'; otherwise, it is an +*> output argument. +*> \endverbatim +*> +*> \param[in,out] R +*> \verbatim +*> R is DOUBLE PRECISION array, dimension (N) +*> The row scale factors for A. If EQUED = 'R' or 'B', A is +*> multiplied on the left by diag(R); if EQUED = 'N' or 'C', R +*> is not accessed. R is an input argument if FACT = 'F'; +*> otherwise, R is an output argument. If FACT = 'F' and +*> EQUED = 'R' or 'B', each element of R must be positive. +*> \endverbatim +*> +*> \param[in,out] C +*> \verbatim +*> C is DOUBLE PRECISION array, dimension (N) +*> The column scale factors for A. If EQUED = 'C' or 'B', A is +*> multiplied on the right by diag(C); if EQUED = 'N' or 'R', C +*> is not accessed. C is an input argument if FACT = 'F'; +*> otherwise, C is an output argument. If FACT = 'F' and +*> EQUED = 'C' or 'B', each element of C must be positive. +*> \endverbatim +*> +*> \param[in,out] B +*> \verbatim +*> B is COMPLEX*16 array, dimension (LDB,NRHS) +*> On entry, the N-by-NRHS right hand side matrix B. +*> On exit, +*> if EQUED = 'N', B is not modified; +*> if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by +*> diag(R)*B; +*> if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is +*> overwritten by diag(C)*B. +*> \endverbatim +*> +*> \param[in] LDB +*> \verbatim +*> LDB is INTEGER +*> The leading dimension of the array B. LDB >= max(1,N). +*> \endverbatim +*> +*> \param[out] X +*> \verbatim +*> X is COMPLEX*16 array, dimension (LDX,NRHS) +*> If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X +*> to the original system of equations. Note that A and B are +*> modified on exit if EQUED .ne. 'N', and the solution to the +*> equilibrated system is inv(diag(C))*X if TRANS = 'N' and +*> EQUED = 'C' or 'B', or inv(diag(R))*X if TRANS = 'T' or 'C' +*> and EQUED = 'R' or 'B'. +*> \endverbatim +*> +*> \param[in] LDX +*> \verbatim +*> LDX is INTEGER +*> The leading dimension of the array X. LDX >= max(1,N). +*> \endverbatim +*> +*> \param[out] RCOND +*> \verbatim +*> RCOND is DOUBLE PRECISION +*> The estimate of the reciprocal condition number of the matrix +*> A after equilibration (if done). If RCOND is less than the +*> machine precision (in particular, if RCOND = 0), the matrix +*> is singular to working precision. This condition is +*> indicated by a return code of INFO > 0. +*> \endverbatim +*> +*> \param[out] FERR +*> \verbatim +*> FERR is DOUBLE PRECISION array, dimension (NRHS) +*> The estimated forward error bound for each solution vector +*> X(j) (the j-th column of the solution matrix X). +*> If XTRUE is the true solution corresponding to X(j), FERR(j) +*> is an estimated upper bound for the magnitude of the largest +*> element in (X(j) - XTRUE) divided by the magnitude of the +*> largest element in X(j). The estimate is as reliable as +*> the estimate for RCOND, and is almost always a slight +*> overestimate of the true error. +*> \endverbatim +*> +*> \param[out] BERR +*> \verbatim +*> BERR is DOUBLE PRECISION array, dimension (NRHS) +*> The componentwise relative backward error of each solution +*> vector X(j) (i.e., the smallest relative change in +*> any element of A or B that makes X(j) an exact solution). +*> \endverbatim +*> +*> \param[out] WORK +*> \verbatim +*> WORK is COMPLEX*16 array, dimension (2*N) +*> \endverbatim +*> +*> \param[out] RWORK +*> \verbatim +*> RWORK is DOUBLE PRECISION array, dimension (MAX(1,2*N)) +*> On exit, RWORK(1) contains the reciprocal pivot growth +*> factor norm(A)/norm(U). The "max absolute element" norm is +*> used. If RWORK(1) is much less than 1, then the stability +*> of the LU factorization of the (equilibrated) matrix A +*> could be poor. This also means that the solution X, condition +*> estimator RCOND, and forward error bound FERR could be +*> unreliable. If factorization fails with 0 RWORK(1) contains the reciprocal pivot growth factor for the +*> leading INFO columns of A. +*> \endverbatim +*> +*> \param[out] INFO +*> \verbatim +*> INFO is INTEGER +*> = 0: successful exit +*> < 0: if INFO = -i, the i-th argument had an illegal value +*> > 0: if INFO = i, and i is +*> <= N: U(i,i) is exactly zero. The factorization has +*> been completed, but the factor U is exactly +*> singular, so the solution and error bounds +*> could not be computed. RCOND = 0 is returned. +*> = N+1: U is nonsingular, but RCOND is less than machine +*> precision, meaning that the matrix is singular +*> to working precision. Nevertheless, the +*> solution and error bounds are computed because +*> there are a number of situations where the +*> computed solution can be more accurate than the +*> value of RCOND would suggest. +*> \endverbatim +* +* Authors: +* ======== +* +*> \author Univ. of Tennessee +*> \author Univ. of California Berkeley +*> \author Univ. of Colorado Denver +*> \author NAG Ltd. +* +*> \ingroup complex16GEsolve +* +* ===================================================================== + SUBROUTINE ZGESVX( FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, + $ EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, + $ WORK, RWORK, INFO ) +* +* -- LAPACK driver routine -- +* -- LAPACK is a software package provided by Univ. of Tennessee, -- +* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- +* +* .. Scalar Arguments .. + CHARACTER EQUED, FACT, TRANS + INTEGER INFO, LDA, LDAF, LDB, LDX, N, NRHS + DOUBLE PRECISION RCOND +* .. +* .. Array Arguments .. + INTEGER IPIV( * ) + DOUBLE PRECISION BERR( * ), C( * ), FERR( * ), R( * ), + $ RWORK( * ) + COMPLEX*16 A( LDA, * ), AF( LDAF, * ), B( LDB, * ), + $ WORK( * ), X( LDX, * ) +* .. +* +* ===================================================================== +* +* .. Parameters .. + DOUBLE PRECISION ZERO, ONE + PARAMETER ( ZERO = 0.0D+0, ONE = 1.0D+0 ) +* .. +* .. Local Scalars .. + LOGICAL COLEQU, EQUIL, NOFACT, NOTRAN, ROWEQU + CHARACTER NORM + INTEGER I, INFEQU, J + DOUBLE PRECISION AMAX, ANORM, BIGNUM, COLCND, RCMAX, RCMIN, + $ ROWCND, RPVGRW, SMLNUM +* .. +* .. External Functions .. + LOGICAL LSAME + DOUBLE PRECISION DLAMCH, ZLANGE, ZLANTR + EXTERNAL LSAME, DLAMCH, ZLANGE, ZLANTR +* .. +* .. External Subroutines .. + EXTERNAL XERBLA, ZGECON, ZGEEQU, ZGERFS, ZGETRF, ZGETRS, + $ ZLACPY, ZLAQGE +* .. +* .. Intrinsic Functions .. + INTRINSIC MAX, MIN +* .. +* .. Executable Statements .. +* + INFO = 0 + NOFACT = LSAME( FACT, 'N' ) + EQUIL = LSAME( FACT, 'E' ) + NOTRAN = LSAME( TRANS, 'N' ) + IF( NOFACT .OR. EQUIL ) THEN + EQUED = 'N' + ROWEQU = .FALSE. + COLEQU = .FALSE. + ELSE + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + SMLNUM = DLAMCH( 'Safe minimum' ) + BIGNUM = ONE / SMLNUM + END IF +* +* Test the input parameters. +* + IF( .NOT.NOFACT .AND. .NOT.EQUIL .AND. .NOT.LSAME( FACT, 'F' ) ) + $ THEN + INFO = -1 + ELSE IF( .NOT.NOTRAN .AND. .NOT.LSAME( TRANS, 'T' ) .AND. .NOT. + $ LSAME( TRANS, 'C' ) ) THEN + INFO = -2 + ELSE IF( N.LT.0 ) THEN + INFO = -3 + ELSE IF( NRHS.LT.0 ) THEN + INFO = -4 + ELSE IF( LDA.LT.MAX( 1, N ) ) THEN + INFO = -6 + ELSE IF( LDAF.LT.MAX( 1, N ) ) THEN + INFO = -8 + ELSE IF( LSAME( FACT, 'F' ) .AND. .NOT. + $ ( ROWEQU .OR. COLEQU .OR. LSAME( EQUED, 'N' ) ) ) THEN + INFO = -10 + ELSE + IF( ROWEQU ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 10 J = 1, N + RCMIN = MIN( RCMIN, R( J ) ) + RCMAX = MAX( RCMAX, R( J ) ) + 10 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -11 + ELSE IF( N.GT.0 ) THEN + ROWCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + ROWCND = ONE + END IF + END IF + IF( COLEQU .AND. INFO.EQ.0 ) THEN + RCMIN = BIGNUM + RCMAX = ZERO + DO 20 J = 1, N + RCMIN = MIN( RCMIN, C( J ) ) + RCMAX = MAX( RCMAX, C( J ) ) + 20 CONTINUE + IF( RCMIN.LE.ZERO ) THEN + INFO = -12 + ELSE IF( N.GT.0 ) THEN + COLCND = MAX( RCMIN, SMLNUM ) / MIN( RCMAX, BIGNUM ) + ELSE + COLCND = ONE + END IF + END IF + IF( INFO.EQ.0 ) THEN + IF( LDB.LT.MAX( 1, N ) ) THEN + INFO = -14 + ELSE IF( LDX.LT.MAX( 1, N ) ) THEN + INFO = -16 + END IF + END IF + END IF +* + IF( INFO.NE.0 ) THEN + CALL XERBLA( 'ZGESVX', -INFO ) + RETURN + END IF +* + IF( EQUIL ) THEN +* +* Compute row and column scalings to equilibrate the matrix A. +* + CALL ZGEEQU( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, INFEQU ) + IF( INFEQU.EQ.0 ) THEN +* +* Equilibrate the matrix. +* + CALL ZLAQGE( N, N, A, LDA, R, C, ROWCND, COLCND, AMAX, + $ EQUED ) + ROWEQU = LSAME( EQUED, 'R' ) .OR. LSAME( EQUED, 'B' ) + COLEQU = LSAME( EQUED, 'C' ) .OR. LSAME( EQUED, 'B' ) + END IF + END IF +* +* Scale the right hand side. +* + IF( NOTRAN ) THEN + IF( ROWEQU ) THEN + DO 40 J = 1, NRHS + DO 30 I = 1, N + B( I, J ) = R( I )*B( I, J ) + 30 CONTINUE + 40 CONTINUE + END IF + ELSE IF( COLEQU ) THEN + DO 60 J = 1, NRHS + DO 50 I = 1, N + B( I, J ) = C( I )*B( I, J ) + 50 CONTINUE + 60 CONTINUE + END IF +* + IF( NOFACT .OR. EQUIL ) THEN +* +* Compute the LU factorization of A. +* + CALL ZLACPY( 'Full', N, N, A, LDA, AF, LDAF ) + CALL ZGETRF( N, N, AF, LDAF, IPIV, INFO ) +* +* Return if INFO is non-zero. +* + IF( INFO.GT.0 ) THEN +* +* Compute the reciprocal pivot growth factor of the +* leading rank-deficient INFO columns of A. +* + RPVGRW = ZLANTR( 'M', 'U', 'N', INFO, INFO, AF, LDAF, + $ RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ZLANGE( 'M', N, INFO, A, LDA, RWORK ) / + $ RPVGRW + END IF + RWORK( 1 ) = RPVGRW + RCOND = ZERO + RETURN + END IF + END IF +* +* Compute the norm of the matrix A and the +* reciprocal pivot growth factor RPVGRW. +* + IF( NOTRAN ) THEN + NORM = '1' + ELSE + NORM = 'I' + END IF + ANORM = ZLANGE( NORM, N, N, A, LDA, RWORK ) + RPVGRW = ZLANTR( 'M', 'U', 'N', N, N, AF, LDAF, RWORK ) + IF( RPVGRW.EQ.ZERO ) THEN + RPVGRW = ONE + ELSE + RPVGRW = ZLANGE( 'M', N, N, A, LDA, RWORK ) / RPVGRW + END IF +* +* Compute the reciprocal of the condition number of A. +* + CALL ZGECON( NORM, N, AF, LDAF, ANORM, RCOND, WORK, RWORK, INFO ) +* +* Compute the solution matrix X. +* + CALL ZLACPY( 'Full', N, NRHS, B, LDB, X, LDX ) + CALL ZGETRS( TRANS, N, NRHS, AF, LDAF, IPIV, X, LDX, INFO ) +* +* Use iterative refinement to improve the computed solution and +* compute error bounds and backward error estimates for it. +* + CALL ZGERFS( TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, B, LDB, X, + $ LDX, FERR, BERR, WORK, RWORK, INFO ) +* +* Transform the solution matrix X to a solution of the original +* system. +* + IF( NOTRAN ) THEN + IF( COLEQU ) THEN + DO 80 J = 1, NRHS + DO 70 I = 1, N + X( I, J ) = C( I )*X( I, J ) + 70 CONTINUE + 80 CONTINUE + DO 90 J = 1, NRHS + FERR( J ) = FERR( J ) / COLCND + 90 CONTINUE + END IF + ELSE IF( ROWEQU ) THEN + DO 110 J = 1, NRHS + DO 100 I = 1, N + X( I, J ) = R( I )*X( I, J ) + 100 CONTINUE + 110 CONTINUE + DO 120 J = 1, NRHS + FERR( J ) = FERR( J ) / ROWCND + 120 CONTINUE + END IF +* +* Set INFO = N+1 if the matrix is singular to working precision. +* + IF( RCOND.LT.DLAMCH( 'Epsilon' ) ) + $ INFO = N + 1 +* + RWORK( 1 ) = RPVGRW + RETURN +* +* End of ZGESVX +* + END