324 lines
10 KiB
Fortran
324 lines
10 KiB
Fortran
*> \brief \b SLATDF uses the LU factorization of the n-by-n matrix computed by sgetc2 and computes a contribution to the reciprocal Dif-estimate.
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*
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* =========== DOCUMENTATION ===========
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*
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* Online html documentation available at
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* http://www.netlib.org/lapack/explore-html/
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*
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*> \htmlonly
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*> Download SLATDF + dependencies
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*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/slatdf.f">
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*> [TGZ]</a>
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*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/slatdf.f">
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*> [ZIP]</a>
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*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/slatdf.f">
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*> [TXT]</a>
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*> \endhtmlonly
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*
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* Definition:
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* ===========
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*
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* SUBROUTINE SLATDF( IJOB, N, Z, LDZ, RHS, RDSUM, RDSCAL, IPIV,
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* JPIV )
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*
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* .. Scalar Arguments ..
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* INTEGER IJOB, LDZ, N
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* REAL RDSCAL, RDSUM
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* ..
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* .. Array Arguments ..
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* INTEGER IPIV( * ), JPIV( * )
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* REAL RHS( * ), Z( LDZ, * )
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* ..
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*
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*
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*> \par Purpose:
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* =============
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*>
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*> \verbatim
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*>
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*> SLATDF uses the LU factorization of the n-by-n matrix Z computed by
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*> SGETC2 and computes a contribution to the reciprocal Dif-estimate
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*> by solving Z * x = b for x, and choosing the r.h.s. b such that
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*> the norm of x is as large as possible. On entry RHS = b holds the
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*> contribution from earlier solved sub-systems, and on return RHS = x.
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*>
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*> The factorization of Z returned by SGETC2 has the form Z = P*L*U*Q,
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*> where P and Q are permutation matrices. L is lower triangular with
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*> unit diagonal elements and U is upper triangular.
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*> \endverbatim
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*
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* Arguments:
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* ==========
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*
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*> \param[in] IJOB
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*> \verbatim
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*> IJOB is INTEGER
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*> IJOB = 2: First compute an approximative null-vector e
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*> of Z using SGECON, e is normalized and solve for
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*> Zx = +-e - f with the sign giving the greater value
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*> of 2-norm(x). About 5 times as expensive as Default.
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*> IJOB .ne. 2: Local look ahead strategy where all entries of
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*> the r.h.s. b is choosen as either +1 or -1 (Default).
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*> \endverbatim
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*>
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*> \param[in] N
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*> \verbatim
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*> N is INTEGER
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*> The number of columns of the matrix Z.
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*> \endverbatim
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*>
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*> \param[in] Z
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*> \verbatim
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*> Z is REAL array, dimension (LDZ, N)
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*> On entry, the LU part of the factorization of the n-by-n
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*> matrix Z computed by SGETC2: Z = P * L * U * Q
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*> \endverbatim
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*>
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*> \param[in] LDZ
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*> \verbatim
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*> LDZ is INTEGER
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*> The leading dimension of the array Z. LDA >= max(1, N).
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*> \endverbatim
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*>
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*> \param[in,out] RHS
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*> \verbatim
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*> RHS is REAL array, dimension N.
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*> On entry, RHS contains contributions from other subsystems.
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*> On exit, RHS contains the solution of the subsystem with
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*> entries acoording to the value of IJOB (see above).
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*> \endverbatim
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*>
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*> \param[in,out] RDSUM
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*> \verbatim
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*> RDSUM is REAL
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*> On entry, the sum of squares of computed contributions to
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*> the Dif-estimate under computation by STGSYL, where the
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*> scaling factor RDSCAL (see below) has been factored out.
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*> On exit, the corresponding sum of squares updated with the
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*> contributions from the current sub-system.
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*> If TRANS = 'T' RDSUM is not touched.
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*> NOTE: RDSUM only makes sense when STGSY2 is called by STGSYL.
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*> \endverbatim
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*>
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*> \param[in,out] RDSCAL
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*> \verbatim
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*> RDSCAL is REAL
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*> On entry, scaling factor used to prevent overflow in RDSUM.
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*> On exit, RDSCAL is updated w.r.t. the current contributions
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*> in RDSUM.
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*> If TRANS = 'T', RDSCAL is not touched.
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*> NOTE: RDSCAL only makes sense when STGSY2 is called by
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*> STGSYL.
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*> \endverbatim
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*>
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*> \param[in] IPIV
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*> \verbatim
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*> IPIV is INTEGER array, dimension (N).
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*> The pivot indices; for 1 <= i <= N, row i of the
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*> matrix has been interchanged with row IPIV(i).
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*> \endverbatim
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*>
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*> \param[in] JPIV
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*> \verbatim
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*> JPIV is INTEGER array, dimension (N).
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*> The pivot indices; for 1 <= j <= N, column j of the
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*> matrix has been interchanged with column JPIV(j).
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*> \endverbatim
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*
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* Authors:
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* ========
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*
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*> \author Univ. of Tennessee
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*> \author Univ. of California Berkeley
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*> \author Univ. of Colorado Denver
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*> \author NAG Ltd.
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*
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*> \date September 2012
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*
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*> \ingroup realOTHERauxiliary
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*
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*> \par Further Details:
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* =====================
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*>
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*> This routine is a further developed implementation of algorithm
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*> BSOLVE in [1] using complete pivoting in the LU factorization.
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*
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*> \par Contributors:
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* ==================
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*>
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*> Bo Kagstrom and Peter Poromaa, Department of Computing Science,
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*> Umea University, S-901 87 Umea, Sweden.
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*
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*> \par References:
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* ================
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*>
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*> \verbatim
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*>
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*>
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*> [1] Bo Kagstrom and Lars Westin,
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*> Generalized Schur Methods with Condition Estimators for
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*> Solving the Generalized Sylvester Equation, IEEE Transactions
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*> on Automatic Control, Vol. 34, No. 7, July 1989, pp 745-751.
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*>
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*> [2] Peter Poromaa,
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*> On Efficient and Robust Estimators for the Separation
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*> between two Regular Matrix Pairs with Applications in
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*> Condition Estimation. Report IMINF-95.05, Departement of
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*> Computing Science, Umea University, S-901 87 Umea, Sweden, 1995.
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*> \endverbatim
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*>
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* =====================================================================
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SUBROUTINE SLATDF( IJOB, N, Z, LDZ, RHS, RDSUM, RDSCAL, IPIV,
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$ JPIV )
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*
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* -- LAPACK auxiliary routine (version 3.4.2) --
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* -- LAPACK is a software package provided by Univ. of Tennessee, --
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* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
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* September 2012
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*
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* .. Scalar Arguments ..
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INTEGER IJOB, LDZ, N
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REAL RDSCAL, RDSUM
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* ..
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* .. Array Arguments ..
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INTEGER IPIV( * ), JPIV( * )
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REAL RHS( * ), Z( LDZ, * )
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* ..
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*
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* =====================================================================
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*
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* .. Parameters ..
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INTEGER MAXDIM
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PARAMETER ( MAXDIM = 8 )
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REAL ZERO, ONE
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PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 )
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* ..
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* .. Local Scalars ..
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INTEGER I, INFO, J, K
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REAL BM, BP, PMONE, SMINU, SPLUS, TEMP
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* ..
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* .. Local Arrays ..
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INTEGER IWORK( MAXDIM )
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REAL WORK( 4*MAXDIM ), XM( MAXDIM ), XP( MAXDIM )
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* ..
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* .. External Subroutines ..
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EXTERNAL SAXPY, SCOPY, SGECON, SGESC2, SLASSQ, SLASWP,
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$ SSCAL
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* ..
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* .. External Functions ..
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REAL SASUM, SDOT
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EXTERNAL SASUM, SDOT
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* ..
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* .. Intrinsic Functions ..
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INTRINSIC ABS, SQRT
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* ..
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* .. Executable Statements ..
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*
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IF( IJOB.NE.2 ) THEN
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*
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* Apply permutations IPIV to RHS
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*
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CALL SLASWP( 1, RHS, LDZ, 1, N-1, IPIV, 1 )
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*
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* Solve for L-part choosing RHS either to +1 or -1.
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*
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PMONE = -ONE
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*
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DO 10 J = 1, N - 1
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BP = RHS( J ) + ONE
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BM = RHS( J ) - ONE
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SPLUS = ONE
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*
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* Look-ahead for L-part RHS(1:N-1) = + or -1, SPLUS and
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* SMIN computed more efficiently than in BSOLVE [1].
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*
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SPLUS = SPLUS + SDOT( N-J, Z( J+1, J ), 1, Z( J+1, J ), 1 )
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SMINU = SDOT( N-J, Z( J+1, J ), 1, RHS( J+1 ), 1 )
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SPLUS = SPLUS*RHS( J )
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IF( SPLUS.GT.SMINU ) THEN
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RHS( J ) = BP
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ELSE IF( SMINU.GT.SPLUS ) THEN
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RHS( J ) = BM
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ELSE
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*
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* In this case the updating sums are equal and we can
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* choose RHS(J) +1 or -1. The first time this happens
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* we choose -1, thereafter +1. This is a simple way to
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* get good estimates of matrices like Byers well-known
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* example (see [1]). (Not done in BSOLVE.)
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*
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RHS( J ) = RHS( J ) + PMONE
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PMONE = ONE
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END IF
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*
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* Compute the remaining r.h.s.
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*
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TEMP = -RHS( J )
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CALL SAXPY( N-J, TEMP, Z( J+1, J ), 1, RHS( J+1 ), 1 )
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*
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10 CONTINUE
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*
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* Solve for U-part, look-ahead for RHS(N) = +-1. This is not done
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* in BSOLVE and will hopefully give us a better estimate because
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* any ill-conditioning of the original matrix is transfered to U
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* and not to L. U(N, N) is an approximation to sigma_min(LU).
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*
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CALL SCOPY( N-1, RHS, 1, XP, 1 )
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XP( N ) = RHS( N ) + ONE
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RHS( N ) = RHS( N ) - ONE
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SPLUS = ZERO
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SMINU = ZERO
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DO 30 I = N, 1, -1
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TEMP = ONE / Z( I, I )
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XP( I ) = XP( I )*TEMP
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RHS( I ) = RHS( I )*TEMP
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DO 20 K = I + 1, N
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XP( I ) = XP( I ) - XP( K )*( Z( I, K )*TEMP )
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RHS( I ) = RHS( I ) - RHS( K )*( Z( I, K )*TEMP )
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20 CONTINUE
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SPLUS = SPLUS + ABS( XP( I ) )
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SMINU = SMINU + ABS( RHS( I ) )
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30 CONTINUE
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IF( SPLUS.GT.SMINU )
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$ CALL SCOPY( N, XP, 1, RHS, 1 )
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*
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* Apply the permutations JPIV to the computed solution (RHS)
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*
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CALL SLASWP( 1, RHS, LDZ, 1, N-1, JPIV, -1 )
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*
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* Compute the sum of squares
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*
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CALL SLASSQ( N, RHS, 1, RDSCAL, RDSUM )
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*
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ELSE
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*
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* IJOB = 2, Compute approximate nullvector XM of Z
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*
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CALL SGECON( 'I', N, Z, LDZ, ONE, TEMP, WORK, IWORK, INFO )
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CALL SCOPY( N, WORK( N+1 ), 1, XM, 1 )
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*
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* Compute RHS
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*
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CALL SLASWP( 1, XM, LDZ, 1, N-1, IPIV, -1 )
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TEMP = ONE / SQRT( SDOT( N, XM, 1, XM, 1 ) )
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CALL SSCAL( N, TEMP, XM, 1 )
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CALL SCOPY( N, XM, 1, XP, 1 )
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CALL SAXPY( N, ONE, RHS, 1, XP, 1 )
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CALL SAXPY( N, -ONE, XM, 1, RHS, 1 )
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CALL SGESC2( N, Z, LDZ, RHS, IPIV, JPIV, TEMP )
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CALL SGESC2( N, Z, LDZ, XP, IPIV, JPIV, TEMP )
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IF( SASUM( N, XP, 1 ).GT.SASUM( N, RHS, 1 ) )
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$ CALL SCOPY( N, XP, 1, RHS, 1 )
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*
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* Compute the sum of squares
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*
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CALL SLASSQ( N, RHS, 1, RDSCAL, RDSUM )
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*
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END IF
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*
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RETURN
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*
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* End of SLATDF
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*
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END
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