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Werner Saar
2017-01-06 11:44:57 +01:00
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lapack-netlib/TESTING/EIG/sdrvsx.f
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*> \brief \b SDRVSX
*
* =========== DOCUMENTATION ===========
*
* Online html documentation available at
* http://www.netlib.org/lapack/explore-html/
*
* Definition:
* ===========
*
* SUBROUTINE SDRVSX( NSIZES, NN, NTYPES, DOTYPE, ISEED, THRESH,
* NIUNIT, NOUNIT, A, LDA, H, HT, WR, WI, WRT,
* WIT, WRTMP, WITMP, VS, LDVS, VS1, RESULT, WORK,
* LWORK, IWORK, BWORK, INFO )
*
* .. Scalar Arguments ..
* INTEGER INFO, LDA, LDVS, LWORK, NIUNIT, NOUNIT, NSIZES,
* $ NTYPES
* REAL THRESH
* ..
* .. Array Arguments ..
* LOGICAL BWORK( * ), DOTYPE( * )
* INTEGER ISEED( 4 ), IWORK( * ), NN( * )
* REAL A( LDA, * ), H( LDA, * ), HT( LDA, * ),
* $ RESULT( 17 ), VS( LDVS, * ), VS1( LDVS, * ),
* $ WI( * ), WIT( * ), WITMP( * ), WORK( * ),
* $ WR( * ), WRT( * ), WRTMP( * )
* ..
*
*
*> \par Purpose:
* =============
*>
*> \verbatim
*>
*> SDRVSX checks the nonsymmetric eigenvalue (Schur form) problem
*> expert driver SGEESX.
*>
*> SDRVSX uses both test matrices generated randomly depending on
*> data supplied in the calling sequence, as well as on data
*> read from an input file and including precomputed condition
*> numbers to which it compares the ones it computes.
*>
*> When SDRVSX is called, a number of matrix "sizes" ("n's") and a
*> number of matrix "types" are specified. For each size ("n")
*> and each type of matrix, one matrix will be generated and used
*> to test the nonsymmetric eigenroutines. For each matrix, 15
*> tests will be performed:
*>
*> (1) 0 if T is in Schur form, 1/ulp otherwise
*> (no sorting of eigenvalues)
*>
*> (2) | A - VS T VS' | / ( n |A| ulp )
*>
*> Here VS is the matrix of Schur eigenvectors, and T is in Schur
*> form (no sorting of eigenvalues).
*>
*> (3) | I - VS VS' | / ( n ulp ) (no sorting of eigenvalues).
*>
*> (4) 0 if WR+sqrt(-1)*WI are eigenvalues of T
*> 1/ulp otherwise
*> (no sorting of eigenvalues)
*>
*> (5) 0 if T(with VS) = T(without VS),
*> 1/ulp otherwise
*> (no sorting of eigenvalues)
*>
*> (6) 0 if eigenvalues(with VS) = eigenvalues(without VS),
*> 1/ulp otherwise
*> (no sorting of eigenvalues)
*>
*> (7) 0 if T is in Schur form, 1/ulp otherwise
*> (with sorting of eigenvalues)
*>
*> (8) | A - VS T VS' | / ( n |A| ulp )
*>
*> Here VS is the matrix of Schur eigenvectors, and T is in Schur
*> form (with sorting of eigenvalues).
*>
*> (9) | I - VS VS' | / ( n ulp ) (with sorting of eigenvalues).
*>
*> (10) 0 if WR+sqrt(-1)*WI are eigenvalues of T
*> 1/ulp otherwise
*> If workspace sufficient, also compare WR, WI with and
*> without reciprocal condition numbers
*> (with sorting of eigenvalues)
*>
*> (11) 0 if T(with VS) = T(without VS),
*> 1/ulp otherwise
*> If workspace sufficient, also compare T with and without
*> reciprocal condition numbers
*> (with sorting of eigenvalues)
*>
*> (12) 0 if eigenvalues(with VS) = eigenvalues(without VS),
*> 1/ulp otherwise
*> If workspace sufficient, also compare VS with and without
*> reciprocal condition numbers
*> (with sorting of eigenvalues)
*>
*> (13) if sorting worked and SDIM is the number of
*> eigenvalues which were SELECTed
*> If workspace sufficient, also compare SDIM with and
*> without reciprocal condition numbers
*>
*> (14) if RCONDE the same no matter if VS and/or RCONDV computed
*>
*> (15) if RCONDV the same no matter if VS and/or RCONDE computed
*>
*> The "sizes" are specified by an array NN(1:NSIZES); the value of
*> each element NN(j) specifies one size.
*> The "types" are specified by a logical array DOTYPE( 1:NTYPES );
*> if DOTYPE(j) is .TRUE., then matrix type "j" will be generated.
*> Currently, the list of possible types is:
*>
*> (1) The zero matrix.
*> (2) The identity matrix.
*> (3) A (transposed) Jordan block, with 1's on the diagonal.
*>
*> (4) A diagonal matrix with evenly spaced entries
*> 1, ..., ULP and random signs.
*> (ULP = (first number larger than 1) - 1 )
*> (5) A diagonal matrix with geometrically spaced entries
*> 1, ..., ULP and random signs.
*> (6) A diagonal matrix with "clustered" entries 1, ULP, ..., ULP
*> and random signs.
*>
*> (7) Same as (4), but multiplied by a constant near
*> the overflow threshold
*> (8) Same as (4), but multiplied by a constant near
*> the underflow threshold
*>
*> (9) A matrix of the form U' T U, where U is orthogonal and
*> T has evenly spaced entries 1, ..., ULP with random signs
*> on the diagonal and random O(1) entries in the upper
*> triangle.
*>
*> (10) A matrix of the form U' T U, where U is orthogonal and
*> T has geometrically spaced entries 1, ..., ULP with random
*> signs on the diagonal and random O(1) entries in the upper
*> triangle.
*>
*> (11) A matrix of the form U' T U, where U is orthogonal and
*> T has "clustered" entries 1, ULP,..., ULP with random
*> signs on the diagonal and random O(1) entries in the upper
*> triangle.
*>
*> (12) A matrix of the form U' T U, where U is orthogonal and
*> T has real or complex conjugate paired eigenvalues randomly
*> chosen from ( ULP, 1 ) and random O(1) entries in the upper
*> triangle.
*>
*> (13) A matrix of the form X' T X, where X has condition
*> SQRT( ULP ) and T has evenly spaced entries 1, ..., ULP
*> with random signs on the diagonal and random O(1) entries
*> in the upper triangle.
*>
*> (14) A matrix of the form X' T X, where X has condition
*> SQRT( ULP ) and T has geometrically spaced entries
*> 1, ..., ULP with random signs on the diagonal and random
*> O(1) entries in the upper triangle.
*>
*> (15) A matrix of the form X' T X, where X has condition
*> SQRT( ULP ) and T has "clustered" entries 1, ULP,..., ULP
*> with random signs on the diagonal and random O(1) entries
*> in the upper triangle.
*>
*> (16) A matrix of the form X' T X, where X has condition
*> SQRT( ULP ) and T has real or complex conjugate paired
*> eigenvalues randomly chosen from ( ULP, 1 ) and random
*> O(1) entries in the upper triangle.
*>
*> (17) Same as (16), but multiplied by a constant
*> near the overflow threshold
*> (18) Same as (16), but multiplied by a constant
*> near the underflow threshold
*>
*> (19) Nonsymmetric matrix with random entries chosen from (-1,1).
*> If N is at least 4, all entries in first two rows and last
*> row, and first column and last two columns are zero.
*> (20) Same as (19), but multiplied by a constant
*> near the overflow threshold
*> (21) Same as (19), but multiplied by a constant
*> near the underflow threshold
*>
*> In addition, an input file will be read from logical unit number
*> NIUNIT. The file contains matrices along with precomputed
*> eigenvalues and reciprocal condition numbers for the eigenvalue
*> average and right invariant subspace. For these matrices, in
*> addition to tests (1) to (15) we will compute the following two
*> tests:
*>
*> (16) |RCONDE - RCDEIN| / cond(RCONDE)
*>
*> RCONDE is the reciprocal average eigenvalue condition number
*> computed by SGEESX and RCDEIN (the precomputed true value)
*> is supplied as input. cond(RCONDE) is the condition number
*> of RCONDE, and takes errors in computing RCONDE into account,
*> so that the resulting quantity should be O(ULP). cond(RCONDE)
*> is essentially given by norm(A)/RCONDV.
*>
*> (17) |RCONDV - RCDVIN| / cond(RCONDV)
*>
*> RCONDV is the reciprocal right invariant subspace condition
*> number computed by SGEESX and RCDVIN (the precomputed true
*> value) is supplied as input. cond(RCONDV) is the condition
*> number of RCONDV, and takes errors in computing RCONDV into
*> account, so that the resulting quantity should be O(ULP).
*> cond(RCONDV) is essentially given by norm(A)/RCONDE.
*> \endverbatim
*
* Arguments:
* ==========
*
*> \param[in] NSIZES
*> \verbatim
*> NSIZES is INTEGER
*> The number of sizes of matrices to use. NSIZES must be at
*> least zero. If it is zero, no randomly generated matrices
*> are tested, but any test matrices read from NIUNIT will be
*> tested.
*> \endverbatim
*>
*> \param[in] NN
*> \verbatim
*> NN is INTEGER array, dimension (NSIZES)
*> An array containing the sizes to be used for the matrices.
*> Zero values will be skipped. The values must be at least
*> zero.
*> \endverbatim
*>
*> \param[in] NTYPES
*> \verbatim
*> NTYPES is INTEGER
*> The number of elements in DOTYPE. NTYPES must be at least
*> zero. If it is zero, no randomly generated test matrices
*> are tested, but and test matrices read from NIUNIT will be
*> tested. If it is MAXTYP+1 and NSIZES is 1, then an
*> additional type, MAXTYP+1 is defined, which is to use
*> whatever matrix is in A. This is only useful if
*> DOTYPE(1:MAXTYP) is .FALSE. and DOTYPE(MAXTYP+1) is .TRUE. .
*> \endverbatim
*>
*> \param[in] DOTYPE
*> \verbatim
*> DOTYPE is LOGICAL array, dimension (NTYPES)
*> If DOTYPE(j) is .TRUE., then for each size in NN a
*> matrix of that size and of type j will be generated.
*> If NTYPES is smaller than the maximum number of types
*> defined (PARAMETER MAXTYP), then types NTYPES+1 through
*> MAXTYP will not be generated. If NTYPES is larger
*> than MAXTYP, DOTYPE(MAXTYP+1) through DOTYPE(NTYPES)
*> will be ignored.
*> \endverbatim
*>
*> \param[in,out] ISEED
*> \verbatim
*> ISEED is INTEGER array, dimension (4)
*> On entry ISEED specifies the seed of the random number
*> generator. The array elements should be between 0 and 4095;
*> if not they will be reduced mod 4096. Also, ISEED(4) must
*> be odd. The random number generator uses a linear
*> congruential sequence limited to small integers, and so
*> should produce machine independent random numbers. The
*> values of ISEED are changed on exit, and can be used in the
*> next call to SDRVSX to continue the same random number
*> sequence.
*> \endverbatim
*>
*> \param[in] THRESH
*> \verbatim
*> THRESH is REAL
*> A test will count as "failed" if the "error", computed as
*> described above, exceeds THRESH. Note that the error
*> is scaled to be O(1), so THRESH should be a reasonably
*> small multiple of 1, e.g., 10 or 100. In particular,
*> it should not depend on the precision (single vs. double)
*> or the size of the matrix. It must be at least zero.
*> \endverbatim
*>
*> \param[in] NIUNIT
*> \verbatim
*> NIUNIT is INTEGER
*> The FORTRAN unit number for reading in the data file of
*> problems to solve.
*> \endverbatim
*>
*> \param[in] NOUNIT
*> \verbatim
*> NOUNIT is INTEGER
*> The FORTRAN unit number for printing out error messages
*> (e.g., if a routine returns INFO not equal to 0.)
*> \endverbatim
*>
*> \param[out] A
*> \verbatim
*> A is REAL array, dimension (LDA, max(NN))
*> Used to hold the matrix whose eigenvalues are to be
*> computed. On exit, A contains the last matrix actually used.
*> \endverbatim
*>
*> \param[in] LDA
*> \verbatim
*> LDA is INTEGER
*> The leading dimension of A, and H. LDA must be at
*> least 1 and at least max( NN ).
*> \endverbatim
*>
*> \param[out] H
*> \verbatim
*> H is REAL array, dimension (LDA, max(NN))
*> Another copy of the test matrix A, modified by SGEESX.
*> \endverbatim
*>
*> \param[out] HT
*> \verbatim
*> HT is REAL array, dimension (LDA, max(NN))
*> Yet another copy of the test matrix A, modified by SGEESX.
*> \endverbatim
*>
*> \param[out] WR
*> \verbatim
*> WR is REAL array, dimension (max(NN))
*> \endverbatim
*>
*> \param[out] WI
*> \verbatim
*> WI is REAL array, dimension (max(NN))
*>
*> The real and imaginary parts of the eigenvalues of A.
*> On exit, WR + WI*i are the eigenvalues of the matrix in A.
*> \endverbatim
*>
*> \param[out] WRT
*> \verbatim
*> WRT is REAL array, dimension (max(NN))
*> \endverbatim
*>
*> \param[out] WIT
*> \verbatim
*> WIT is REAL array, dimension (max(NN))
*>
*> Like WR, WI, these arrays contain the eigenvalues of A,
*> but those computed when SGEESX only computes a partial
*> eigendecomposition, i.e. not Schur vectors
*> \endverbatim
*>
*> \param[out] WRTMP
*> \verbatim
*> WRTMP is REAL array, dimension (max(NN))
*> \endverbatim
*>
*> \param[out] WITMP
*> \verbatim
*> WITMP is REAL array, dimension (max(NN))
*>
*> More temporary storage for eigenvalues.
*> \endverbatim
*>
*> \param[out] VS
*> \verbatim
*> VS is REAL array, dimension (LDVS, max(NN))
*> VS holds the computed Schur vectors.
*> \endverbatim
*>
*> \param[in] LDVS
*> \verbatim
*> LDVS is INTEGER
*> Leading dimension of VS. Must be at least max(1,max(NN)).
*> \endverbatim
*>
*> \param[out] VS1
*> \verbatim
*> VS1 is REAL array, dimension (LDVS, max(NN))
*> VS1 holds another copy of the computed Schur vectors.
*> \endverbatim
*>
*> \param[out] RESULT
*> \verbatim
*> RESULT is REAL array, dimension (17)
*> The values computed by the 17 tests described above.
*> The values are currently limited to 1/ulp, to avoid overflow.
*> \endverbatim
*>
*> \param[out] WORK
*> \verbatim
*> WORK is REAL array, dimension (LWORK)
*> \endverbatim
*>
*> \param[in] LWORK
*> \verbatim
*> LWORK is INTEGER
*> The number of entries in WORK. This must be at least
*> max(3*NN(j),2*NN(j)**2) for all j.
*> \endverbatim
*>
*> \param[out] IWORK
*> \verbatim
*> IWORK is INTEGER array, dimension (max(NN)*max(NN))
*> \endverbatim
*>
*> \param[out] BWORK
*> \verbatim
*> BWORK is LOGICAL array, dimension (max(NN))
*> \endverbatim
*>
*> \param[out] INFO
*> \verbatim
*> INFO is INTEGER
*> If 0, successful exit.
*> <0, input parameter -INFO is incorrect
*> >0, SLATMR, SLATMS, SLATME or SGET24 returned an error
*> code and INFO is its absolute value
*>
*>-----------------------------------------------------------------------
*>
*> Some Local Variables and Parameters:
*> ---- ----- --------- --- ----------
*> ZERO, ONE Real 0 and 1.
*> MAXTYP The number of types defined.
*> NMAX Largest value in NN.
*> NERRS The number of tests which have exceeded THRESH
*> COND, CONDS,
*> IMODE Values to be passed to the matrix generators.
*> ANORM Norm of A; passed to matrix generators.
*>
*> OVFL, UNFL Overflow and underflow thresholds.
*> ULP, ULPINV Finest relative precision and its inverse.
*> RTULP, RTULPI Square roots of the previous 4 values.
*> The following four arrays decode JTYPE:
*> KTYPE(j) The general type (1-10) for type "j".
*> KMODE(j) The MODE value to be passed to the matrix
*> generator for type "j".
*> KMAGN(j) The order of magnitude ( O(1),
*> O(overflow^(1/2) ), O(underflow^(1/2) )
*> KCONDS(j) Selectw whether CONDS is to be 1 or
*> 1/sqrt(ulp). (0 means irrelevant.)
*> \endverbatim
*
* Authors:
* ========
*
*> \author Univ. of Tennessee
*> \author Univ. of California Berkeley
*> \author Univ. of Colorado Denver
*> \author NAG Ltd.
*
*> \date June 2016
*
*> \ingroup single_eig
*
* =====================================================================
SUBROUTINE SDRVSX( NSIZES, NN, NTYPES, DOTYPE, ISEED, THRESH,
$ NIUNIT, NOUNIT, A, LDA, H, HT, WR, WI, WRT,
$ WIT, WRTMP, WITMP, VS, LDVS, VS1, RESULT, WORK,
$ LWORK, IWORK, BWORK, INFO )
*
* -- LAPACK test routine (version 3.6.1) --
* -- LAPACK is a software package provided by Univ. of Tennessee, --
* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
* June 2016
*
* .. Scalar Arguments ..
INTEGER INFO, LDA, LDVS, LWORK, NIUNIT, NOUNIT, NSIZES,
$ NTYPES
REAL THRESH
* ..
* .. Array Arguments ..
LOGICAL BWORK( * ), DOTYPE( * )
INTEGER ISEED( 4 ), IWORK( * ), NN( * )
REAL A( LDA, * ), H( LDA, * ), HT( LDA, * ),
$ RESULT( 17 ), VS( LDVS, * ), VS1( LDVS, * ),
$ WI( * ), WIT( * ), WITMP( * ), WORK( * ),
$ WR( * ), WRT( * ), WRTMP( * )
* ..
*
* =====================================================================
*
* .. Parameters ..
REAL ZERO, ONE
PARAMETER ( ZERO = 0.0E0, ONE = 1.0E0 )
INTEGER MAXTYP
PARAMETER ( MAXTYP = 21 )
* ..
* .. Local Scalars ..
LOGICAL BADNN
CHARACTER*3 PATH
INTEGER I, IINFO, IMODE, ITYPE, IWK, J, JCOL, JSIZE,
$ JTYPE, MTYPES, N, NERRS, NFAIL, NMAX,
$ NNWORK, NSLCT, NTEST, NTESTF, NTESTT
REAL ANORM, COND, CONDS, OVFL, RCDEIN, RCDVIN,
$ RTULP, RTULPI, ULP, ULPINV, UNFL
* ..
* .. Local Arrays ..
CHARACTER ADUMMA( 1 )
INTEGER IDUMMA( 1 ), IOLDSD( 4 ), ISLCT( 20 ),
$ KCONDS( MAXTYP ), KMAGN( MAXTYP ),
$ KMODE( MAXTYP ), KTYPE( MAXTYP )
* ..
* .. Arrays in Common ..
LOGICAL SELVAL( 20 )
REAL SELWI( 20 ), SELWR( 20 )
* ..
* .. Scalars in Common ..
INTEGER SELDIM, SELOPT
* ..
* .. Common blocks ..
COMMON / SSLCT / SELOPT, SELDIM, SELVAL, SELWR, SELWI
* ..
* .. External Functions ..
REAL SLAMCH
EXTERNAL SLAMCH
* ..
* .. External Subroutines ..
EXTERNAL SGET24, SLABAD, SLASUM, SLATME, SLATMR, SLATMS,
$ SLASET, XERBLA
* ..
* .. Intrinsic Functions ..
INTRINSIC ABS, MAX, MIN, SQRT
* ..
* .. Data statements ..
DATA KTYPE / 1, 2, 3, 5*4, 4*6, 6*6, 3*9 /
DATA KMAGN / 3*1, 1, 1, 1, 2, 3, 4*1, 1, 1, 1, 1, 2,
$ 3, 1, 2, 3 /
DATA KMODE / 3*0, 4, 3, 1, 4, 4, 4, 3, 1, 5, 4, 3,
$ 1, 5, 5, 5, 4, 3, 1 /
DATA KCONDS / 3*0, 5*0, 4*1, 6*2, 3*0 /
* ..
* .. Executable Statements ..
*
PATH( 1: 1 ) = 'Single precision'
PATH( 2: 3 ) = 'SX'
*
* Check for errors
*
NTESTT = 0
NTESTF = 0
INFO = 0
*
* Important constants
*
BADNN = .FALSE.
*
* 12 is the largest dimension in the input file of precomputed
* problems
*
NMAX = 12
DO 10 J = 1, NSIZES
NMAX = MAX( NMAX, NN( J ) )
IF( NN( J ).LT.0 )
$ BADNN = .TRUE.
10 CONTINUE
*
* Check for errors
*
IF( NSIZES.LT.0 ) THEN
INFO = -1
ELSE IF( BADNN ) THEN
INFO = -2
ELSE IF( NTYPES.LT.0 ) THEN
INFO = -3
ELSE IF( THRESH.LT.ZERO ) THEN
INFO = -6
ELSE IF( NIUNIT.LE.0 ) THEN
INFO = -7
ELSE IF( NOUNIT.LE.0 ) THEN
INFO = -8
ELSE IF( LDA.LT.1 .OR. LDA.LT.NMAX ) THEN
INFO = -10
ELSE IF( LDVS.LT.1 .OR. LDVS.LT.NMAX ) THEN
INFO = -20
ELSE IF( MAX( 3*NMAX, 2*NMAX**2 ).GT.LWORK ) THEN
INFO = -24
END IF
*
IF( INFO.NE.0 ) THEN
CALL XERBLA( 'SDRVSX', -INFO )
RETURN
END IF
*
* If nothing to do check on NIUNIT
*
IF( NSIZES.EQ.0 .OR. NTYPES.EQ.0 )
$ GO TO 150
*
* More Important constants
*
UNFL = SLAMCH( 'Safe minimum' )
OVFL = ONE / UNFL
CALL SLABAD( UNFL, OVFL )
ULP = SLAMCH( 'Precision' )
ULPINV = ONE / ULP
RTULP = SQRT( ULP )
RTULPI = ONE / RTULP
*
* Loop over sizes, types
*
NERRS = 0
*
DO 140 JSIZE = 1, NSIZES
N = NN( JSIZE )
IF( NSIZES.NE.1 ) THEN
MTYPES = MIN( MAXTYP, NTYPES )
ELSE
MTYPES = MIN( MAXTYP+1, NTYPES )
END IF
*
DO 130 JTYPE = 1, MTYPES
IF( .NOT.DOTYPE( JTYPE ) )
$ GO TO 130
*
* Save ISEED in case of an error.
*
DO 20 J = 1, 4
IOLDSD( J ) = ISEED( J )
20 CONTINUE
*
* Compute "A"
*
* Control parameters:
*
* KMAGN KCONDS KMODE KTYPE
* =1 O(1) 1 clustered 1 zero
* =2 large large clustered 2 identity
* =3 small exponential Jordan
* =4 arithmetic diagonal, (w/ eigenvalues)
* =5 random log symmetric, w/ eigenvalues
* =6 random general, w/ eigenvalues
* =7 random diagonal
* =8 random symmetric
* =9 random general
* =10 random triangular
*
IF( MTYPES.GT.MAXTYP )
$ GO TO 90
*
ITYPE = KTYPE( JTYPE )
IMODE = KMODE( JTYPE )
*
* Compute norm
*
GO TO ( 30, 40, 50 )KMAGN( JTYPE )
*
30 CONTINUE
ANORM = ONE
GO TO 60
*
40 CONTINUE
ANORM = OVFL*ULP
GO TO 60
*
50 CONTINUE
ANORM = UNFL*ULPINV
GO TO 60
*
60 CONTINUE
*
CALL SLASET( 'Full', LDA, N, ZERO, ZERO, A, LDA )
IINFO = 0
COND = ULPINV
*
* Special Matrices -- Identity & Jordan block
*
* Zero
*
IF( ITYPE.EQ.1 ) THEN
IINFO = 0
*
ELSE IF( ITYPE.EQ.2 ) THEN
*
* Identity
*
DO 70 JCOL = 1, N
A( JCOL, JCOL ) = ANORM
70 CONTINUE
*
ELSE IF( ITYPE.EQ.3 ) THEN
*
* Jordan Block
*
DO 80 JCOL = 1, N
A( JCOL, JCOL ) = ANORM
IF( JCOL.GT.1 )
$ A( JCOL, JCOL-1 ) = ONE
80 CONTINUE
*
ELSE IF( ITYPE.EQ.4 ) THEN
*
* Diagonal Matrix, [Eigen]values Specified
*
CALL SLATMS( N, N, 'S', ISEED, 'S', WORK, IMODE, COND,
$ ANORM, 0, 0, 'N', A, LDA, WORK( N+1 ),
$ IINFO )
*
ELSE IF( ITYPE.EQ.5 ) THEN
*
* Symmetric, eigenvalues specified
*
CALL SLATMS( N, N, 'S', ISEED, 'S', WORK, IMODE, COND,
$ ANORM, N, N, 'N', A, LDA, WORK( N+1 ),
$ IINFO )
*
ELSE IF( ITYPE.EQ.6 ) THEN
*
* General, eigenvalues specified
*
IF( KCONDS( JTYPE ).EQ.1 ) THEN
CONDS = ONE
ELSE IF( KCONDS( JTYPE ).EQ.2 ) THEN
CONDS = RTULPI
ELSE
CONDS = ZERO
END IF
*
ADUMMA( 1 ) = ' '
CALL SLATME( N, 'S', ISEED, WORK, IMODE, COND, ONE,
$ ADUMMA, 'T', 'T', 'T', WORK( N+1 ), 4,
$ CONDS, N, N, ANORM, A, LDA, WORK( 2*N+1 ),
$ IINFO )
*
ELSE IF( ITYPE.EQ.7 ) THEN
*
* Diagonal, random eigenvalues
*
CALL SLATMR( N, N, 'S', ISEED, 'S', WORK, 6, ONE, ONE,
$ 'T', 'N', WORK( N+1 ), 1, ONE,
$ WORK( 2*N+1 ), 1, ONE, 'N', IDUMMA, 0, 0,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE IF( ITYPE.EQ.8 ) THEN
*
* Symmetric, random eigenvalues
*
CALL SLATMR( N, N, 'S', ISEED, 'S', WORK, 6, ONE, ONE,
$ 'T', 'N', WORK( N+1 ), 1, ONE,
$ WORK( 2*N+1 ), 1, ONE, 'N', IDUMMA, N, N,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE IF( ITYPE.EQ.9 ) THEN
*
* General, random eigenvalues
*
CALL SLATMR( N, N, 'S', ISEED, 'N', WORK, 6, ONE, ONE,
$ 'T', 'N', WORK( N+1 ), 1, ONE,
$ WORK( 2*N+1 ), 1, ONE, 'N', IDUMMA, N, N,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
IF( N.GE.4 ) THEN
CALL SLASET( 'Full', 2, N, ZERO, ZERO, A, LDA )
CALL SLASET( 'Full', N-3, 1, ZERO, ZERO, A( 3, 1 ),
$ LDA )
CALL SLASET( 'Full', N-3, 2, ZERO, ZERO, A( 3, N-1 ),
$ LDA )
CALL SLASET( 'Full', 1, N, ZERO, ZERO, A( N, 1 ),
$ LDA )
END IF
*
ELSE IF( ITYPE.EQ.10 ) THEN
*
* Triangular, random eigenvalues
*
CALL SLATMR( N, N, 'S', ISEED, 'N', WORK, 6, ONE, ONE,
$ 'T', 'N', WORK( N+1 ), 1, ONE,
$ WORK( 2*N+1 ), 1, ONE, 'N', IDUMMA, N, 0,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE
*
IINFO = 1
END IF
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUNIT, FMT = 9991 )'Generator', IINFO, N, JTYPE,
$ IOLDSD
INFO = ABS( IINFO )
RETURN
END IF
*
90 CONTINUE
*
* Test for minimal and generous workspace
*
DO 120 IWK = 1, 2
IF( IWK.EQ.1 ) THEN
NNWORK = 3*N
ELSE
NNWORK = MAX( 3*N, 2*N*N )
END IF
NNWORK = MAX( NNWORK, 1 )
*
CALL SGET24( .FALSE., JTYPE, THRESH, IOLDSD, NOUNIT, N,
$ A, LDA, H, HT, WR, WI, WRT, WIT, WRTMP,
$ WITMP, VS, LDVS, VS1, RCDEIN, RCDVIN, NSLCT,
$ ISLCT, RESULT, WORK, NNWORK, IWORK, BWORK,
$ INFO )
*
* Check for RESULT(j) > THRESH
*
NTEST = 0
NFAIL = 0
DO 100 J = 1, 15
IF( RESULT( J ).GE.ZERO )
$ NTEST = NTEST + 1
IF( RESULT( J ).GE.THRESH )
$ NFAIL = NFAIL + 1
100 CONTINUE
*
IF( NFAIL.GT.0 )
$ NTESTF = NTESTF + 1
IF( NTESTF.EQ.1 ) THEN
WRITE( NOUNIT, FMT = 9999 )PATH
WRITE( NOUNIT, FMT = 9998 )
WRITE( NOUNIT, FMT = 9997 )
WRITE( NOUNIT, FMT = 9996 )
WRITE( NOUNIT, FMT = 9995 )THRESH
WRITE( NOUNIT, FMT = 9994 )
NTESTF = 2
END IF
*
DO 110 J = 1, 15
IF( RESULT( J ).GE.THRESH ) THEN
WRITE( NOUNIT, FMT = 9993 )N, IWK, IOLDSD, JTYPE,
$ J, RESULT( J )
END IF
110 CONTINUE
*
NERRS = NERRS + NFAIL
NTESTT = NTESTT + NTEST
*
120 CONTINUE
130 CONTINUE
140 CONTINUE
*
150 CONTINUE
*
* Read in data from file to check accuracy of condition estimation
* Read input data until N=0
*
JTYPE = 0
160 CONTINUE
READ( NIUNIT, FMT = *, END = 200 )N, NSLCT
IF( N.EQ.0 )
$ GO TO 200
JTYPE = JTYPE + 1
ISEED( 1 ) = JTYPE
IF( NSLCT.GT.0 )
$ READ( NIUNIT, FMT = * )( ISLCT( I ), I = 1, NSLCT )
DO 170 I = 1, N
READ( NIUNIT, FMT = * )( A( I, J ), J = 1, N )
170 CONTINUE
READ( NIUNIT, FMT = * )RCDEIN, RCDVIN
*
CALL SGET24( .TRUE., 22, THRESH, ISEED, NOUNIT, N, A, LDA, H, HT,
$ WR, WI, WRT, WIT, WRTMP, WITMP, VS, LDVS, VS1,
$ RCDEIN, RCDVIN, NSLCT, ISLCT, RESULT, WORK, LWORK,
$ IWORK, BWORK, INFO )
*
* Check for RESULT(j) > THRESH
*
NTEST = 0
NFAIL = 0
DO 180 J = 1, 17
IF( RESULT( J ).GE.ZERO )
$ NTEST = NTEST + 1
IF( RESULT( J ).GE.THRESH )
$ NFAIL = NFAIL + 1
180 CONTINUE
*
IF( NFAIL.GT.0 )
$ NTESTF = NTESTF + 1
IF( NTESTF.EQ.1 ) THEN
WRITE( NOUNIT, FMT = 9999 )PATH
WRITE( NOUNIT, FMT = 9998 )
WRITE( NOUNIT, FMT = 9997 )
WRITE( NOUNIT, FMT = 9996 )
WRITE( NOUNIT, FMT = 9995 )THRESH
WRITE( NOUNIT, FMT = 9994 )
NTESTF = 2
END IF
DO 190 J = 1, 17
IF( RESULT( J ).GE.THRESH ) THEN
WRITE( NOUNIT, FMT = 9992 )N, JTYPE, J, RESULT( J )
END IF
190 CONTINUE
*
NERRS = NERRS + NFAIL
NTESTT = NTESTT + NTEST
GO TO 160
200 CONTINUE
*
* Summary
*
CALL SLASUM( PATH, NOUNIT, NERRS, NTESTT )
*
9999 FORMAT( / 1X, A3, ' -- Real Schur Form Decomposition Expert ',
$ 'Driver', / ' Matrix types (see SDRVSX for details):' )
*
9998 FORMAT( / ' Special Matrices:', / ' 1=Zero matrix. ',
$ ' ', ' 5=Diagonal: geometr. spaced entries.',
$ / ' 2=Identity matrix. ', ' 6=Diagona',
$ 'l: clustered entries.', / ' 3=Transposed Jordan block. ',
$ ' ', ' 7=Diagonal: large, evenly spaced.', / ' ',
$ '4=Diagonal: evenly spaced entries. ', ' 8=Diagonal: s',
$ 'mall, evenly spaced.' )
9997 FORMAT( ' Dense, Non-Symmetric Matrices:', / ' 9=Well-cond., ev',
$ 'enly spaced eigenvals.', ' 14=Ill-cond., geomet. spaced e',
$ 'igenals.', / ' 10=Well-cond., geom. spaced eigenvals. ',
$ ' 15=Ill-conditioned, clustered e.vals.', / ' 11=Well-cond',
$ 'itioned, clustered e.vals. ', ' 16=Ill-cond., random comp',
$ 'lex ', / ' 12=Well-cond., random complex ', ' ',
$ ' 17=Ill-cond., large rand. complx ', / ' 13=Ill-condi',
$ 'tioned, evenly spaced. ', ' 18=Ill-cond., small rand.',
$ ' complx ' )
9996 FORMAT( ' 19=Matrix with random O(1) entries. ', ' 21=Matrix ',
$ 'with small random entries.', / ' 20=Matrix with large ran',
$ 'dom entries. ', / )
9995 FORMAT( ' Tests performed with test threshold =', F8.2,
$ / ' ( A denotes A on input and T denotes A on output)',
$ / / ' 1 = 0 if T in Schur form (no sort), ',
$ ' 1/ulp otherwise', /
$ ' 2 = | A - VS T transpose(VS) | / ( n |A| ulp ) (no sort)',
$ / ' 3 = | I - VS transpose(VS) | / ( n ulp ) (no sort) ', /
$ ' 4 = 0 if WR+sqrt(-1)*WI are eigenvalues of T (no sort),',
$ ' 1/ulp otherwise', /
$ ' 5 = 0 if T same no matter if VS computed (no sort),',
$ ' 1/ulp otherwise', /
$ ' 6 = 0 if WR, WI same no matter if VS computed (no sort)',
$ ', 1/ulp otherwise' )
9994 FORMAT( ' 7 = 0 if T in Schur form (sort), ', ' 1/ulp otherwise',
$ / ' 8 = | A - VS T transpose(VS) | / ( n |A| ulp ) (sort)',
$ / ' 9 = | I - VS transpose(VS) | / ( n ulp ) (sort) ',
$ / ' 10 = 0 if WR+sqrt(-1)*WI are eigenvalues of T (sort),',
$ ' 1/ulp otherwise', /
$ ' 11 = 0 if T same no matter what else computed (sort),',
$ ' 1/ulp otherwise', /
$ ' 12 = 0 if WR, WI same no matter what else computed ',
$ '(sort), 1/ulp otherwise', /
$ ' 13 = 0 if sorting successful, 1/ulp otherwise',
$ / ' 14 = 0 if RCONDE same no matter what else computed,',
$ ' 1/ulp otherwise', /
$ ' 15 = 0 if RCONDv same no matter what else computed,',
$ ' 1/ulp otherwise', /
$ ' 16 = | RCONDE - RCONDE(precomputed) | / cond(RCONDE),',
$ / ' 17 = | RCONDV - RCONDV(precomputed) | / cond(RCONDV),' )
9993 FORMAT( ' N=', I5, ', IWK=', I2, ', seed=', 4( I4, ',' ),
$ ' type ', I2, ', test(', I2, ')=', G10.3 )
9992 FORMAT( ' N=', I5, ', input example =', I3, ', test(', I2, ')=',
$ G10.3 )
9991 FORMAT( ' SDRVSX: ', A, ' returned INFO=', I6, '.', / 9X, 'N=',
$ I6, ', JTYPE=', I6, ', ISEED=(', 3( I5, ',' ), I5, ')' )
*
RETURN
*
* End of SDRVSX
*
END