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Cloned library LAPACK-3.11.0 with extra build files for internal package management.
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LAPACK-3.11.0/TESTING/EIG/schkbd.f

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*> \brief \b SCHKBD
*
* =========== DOCUMENTATION ===========
*
* Online html documentation available at
* http://www.netlib.org/lapack/explore-html/
*
* Definition:
* ===========
*
* SUBROUTINE SCHKBD( NSIZES, MVAL, NVAL, NTYPES, DOTYPE, NRHS,
* ISEED, THRESH, A, LDA, BD, BE, S1, S2, X, LDX,
* Y, Z, Q, LDQ, PT, LDPT, U, VT, WORK, LWORK,
* IWORK, NOUT, INFO )
*
* .. Scalar Arguments ..
* INTEGER INFO, LDA, LDPT, LDQ, LDX, LWORK, NOUT, NRHS,
* $ NSIZES, NTYPES
* REAL THRESH
* ..
* .. Array Arguments ..
* LOGICAL DOTYPE( * )
* INTEGER ISEED( 4 ), IWORK( * ), MVAL( * ), NVAL( * )
* REAL A( LDA, * ), BD( * ), BE( * ), PT( LDPT, * ),
* $ Q( LDQ, * ), S1( * ), S2( * ), U( LDPT, * ),
* $ VT( LDPT, * ), WORK( * ), X( LDX, * ),
* $ Y( LDX, * ), Z( LDX, * )
* ..
*
*
*> \par Purpose:
* =============
*>
*> \verbatim
*>
*> SCHKBD checks the singular value decomposition (SVD) routines.
*>
*> SGEBRD reduces a real general m by n matrix A to upper or lower
*> bidiagonal form B by an orthogonal transformation: Q' * A * P = B
*> (or A = Q * B * P'). The matrix B is upper bidiagonal if m >= n
*> and lower bidiagonal if m < n.
*>
*> SORGBR generates the orthogonal matrices Q and P' from SGEBRD.
*> Note that Q and P are not necessarily square.
*>
*> SBDSQR computes the singular value decomposition of the bidiagonal
*> matrix B as B = U S V'. It is called three times to compute
*> 1) B = U S1 V', where S1 is the diagonal matrix of singular
*> values and the columns of the matrices U and V are the left
*> and right singular vectors, respectively, of B.
*> 2) Same as 1), but the singular values are stored in S2 and the
*> singular vectors are not computed.
*> 3) A = (UQ) S (P'V'), the SVD of the original matrix A.
*> In addition, SBDSQR has an option to apply the left orthogonal matrix
*> U to a matrix X, useful in least squares applications.
*>
*> SBDSDC computes the singular value decomposition of the bidiagonal
*> matrix B as B = U S V' using divide-and-conquer. It is called twice
*> to compute
*> 1) B = U S1 V', where S1 is the diagonal matrix of singular
*> values and the columns of the matrices U and V are the left
*> and right singular vectors, respectively, of B.
*> 2) Same as 1), but the singular values are stored in S2 and the
*> singular vectors are not computed.
*>
*> SBDSVDX computes the singular value decomposition of the bidiagonal
*> matrix B as B = U S V' using bisection and inverse iteration. It is
*> called six times to compute
*> 1) B = U S1 V', RANGE='A', where S1 is the diagonal matrix of singular
*> values and the columns of the matrices U and V are the left
*> and right singular vectors, respectively, of B.
*> 2) Same as 1), but the singular values are stored in S2 and the
*> singular vectors are not computed.
*> 3) B = U S1 V', RANGE='I', with where S1 is the diagonal matrix of singular
*> values and the columns of the matrices U and V are the left
*> and right singular vectors, respectively, of B
*> 4) Same as 3), but the singular values are stored in S2 and the
*> singular vectors are not computed.
*> 5) B = U S1 V', RANGE='V', with where S1 is the diagonal matrix of singular
*> values and the columns of the matrices U and V are the left
*> and right singular vectors, respectively, of B
*> 6) Same as 5), but the singular values are stored in S2 and the
*> singular vectors are not computed.
*>
*> For each pair of matrix dimensions (M,N) and each selected matrix
*> type, an M by N matrix A and an M by NRHS matrix X are generated.
*> The problem dimensions are as follows
*> A: M x N
*> Q: M x min(M,N) (but M x M if NRHS > 0)
*> P: min(M,N) x N
*> B: min(M,N) x min(M,N)
*> U, V: min(M,N) x min(M,N)
*> S1, S2 diagonal, order min(M,N)
*> X: M x NRHS
*>
*> For each generated matrix, 14 tests are performed:
*>
*> Test SGEBRD and SORGBR
*>
*> (1) | A - Q B PT | / ( |A| max(M,N) ulp ), PT = P'
*>
*> (2) | I - Q' Q | / ( M ulp )
*>
*> (3) | I - PT PT' | / ( N ulp )
*>
*> Test SBDSQR on bidiagonal matrix B
*>
*> (4) | B - U S1 VT | / ( |B| min(M,N) ulp ), VT = V'
*>
*> (5) | Y - U Z | / ( |Y| max(min(M,N),k) ulp ), where Y = Q' X
*> and Z = U' Y.
*> (6) | I - U' U | / ( min(M,N) ulp )
*>
*> (7) | I - VT VT' | / ( min(M,N) ulp )
*>
*> (8) S1 contains min(M,N) nonnegative values in decreasing order.
*> (Return 0 if true, 1/ULP if false.)
*>
*> (9) | S1 - S2 | / ( |S1| ulp ), where S2 is computed without
*> computing U and V.
*>
*> (10) 0 if the true singular values of B are within THRESH of
*> those in S1. 2*THRESH if they are not. (Tested using
*> SSVDCH)
*>
*> Test SBDSQR on matrix A
*>
*> (11) | A - (QU) S (VT PT) | / ( |A| max(M,N) ulp )
*>
*> (12) | X - (QU) Z | / ( |X| max(M,k) ulp )
*>
*> (13) | I - (QU)'(QU) | / ( M ulp )
*>
*> (14) | I - (VT PT) (PT'VT') | / ( N ulp )
*>
*> Test SBDSDC on bidiagonal matrix B
*>
*> (15) | B - U S1 VT | / ( |B| min(M,N) ulp ), VT = V'
*>
*> (16) | I - U' U | / ( min(M,N) ulp )
*>
*> (17) | I - VT VT' | / ( min(M,N) ulp )
*>
*> (18) S1 contains min(M,N) nonnegative values in decreasing order.
*> (Return 0 if true, 1/ULP if false.)
*>
*> (19) | S1 - S2 | / ( |S1| ulp ), where S2 is computed without
*> computing U and V.
*> Test SBDSVDX on bidiagonal matrix B
*>
*> (20) | B - U S1 VT | / ( |B| min(M,N) ulp ), VT = V'
*>
*> (21) | I - U' U | / ( min(M,N) ulp )
*>
*> (22) | I - VT VT' | / ( min(M,N) ulp )
*>
*> (23) S1 contains min(M,N) nonnegative values in decreasing order.
*> (Return 0 if true, 1/ULP if false.)
*>
*> (24) | S1 - S2 | / ( |S1| ulp ), where S2 is computed without
*> computing U and V.
*>
*> (25) | S1 - U' B VT' | / ( |S| n ulp ) SBDSVDX('V', 'I')
*>
*> (26) | I - U' U | / ( min(M,N) ulp )
*>
*> (27) | I - VT VT' | / ( min(M,N) ulp )
*>
*> (28) S1 contains min(M,N) nonnegative values in decreasing order.
*> (Return 0 if true, 1/ULP if false.)
*>
*> (29) | S1 - S2 | / ( |S1| ulp ), where S2 is computed without
*> computing U and V.
*>
*> (30) | S1 - U' B VT' | / ( |S1| n ulp ) SBDSVDX('V', 'V')
*>
*> (31) | I - U' U | / ( min(M,N) ulp )
*>
*> (32) | I - VT VT' | / ( min(M,N) ulp )
*>
*> (33) S1 contains min(M,N) nonnegative values in decreasing order.
*> (Return 0 if true, 1/ULP if false.)
*>
*> (34) | S1 - S2 | / ( |S1| ulp ), where S2 is computed without
*> computing U and V.
*>
*> The possible matrix types are
*>
*> (1) The zero matrix.
*> (2) The identity matrix.
*>
*> (3) A diagonal matrix with evenly spaced entries
*> 1, ..., ULP and random signs.
*> (ULP = (first number larger than 1) - 1 )
*> (4) A diagonal matrix with geometrically spaced entries
*> 1, ..., ULP and random signs.
*> (5) A diagonal matrix with "clustered" entries 1, ULP, ..., ULP
*> and random signs.
*>
*> (6) Same as (3), but multiplied by SQRT( overflow threshold )
*> (7) Same as (3), but multiplied by SQRT( underflow threshold )
*>
*> (8) A matrix of the form U D V, where U and V are orthogonal and
*> D has evenly spaced entries 1, ..., ULP with random signs
*> on the diagonal.
*>
*> (9) A matrix of the form U D V, where U and V are orthogonal and
*> D has geometrically spaced entries 1, ..., ULP with random
*> signs on the diagonal.
*>
*> (10) A matrix of the form U D V, where U and V are orthogonal and
*> D has "clustered" entries 1, ULP,..., ULP with random
*> signs on the diagonal.
*>
*> (11) Same as (8), but multiplied by SQRT( overflow threshold )
*> (12) Same as (8), but multiplied by SQRT( underflow threshold )
*>
*> (13) Rectangular matrix with random entries chosen from (-1,1).
*> (14) Same as (13), but multiplied by SQRT( overflow threshold )
*> (15) Same as (13), but multiplied by SQRT( underflow threshold )
*>
*> Special case:
*> (16) A bidiagonal matrix with random entries chosen from a
*> logarithmic distribution on [ulp^2,ulp^(-2)] (I.e., each
*> entry is e^x, where x is chosen uniformly on
*> [ 2 log(ulp), -2 log(ulp) ] .) For *this* type:
*> (a) SGEBRD is not called to reduce it to bidiagonal form.
*> (b) the bidiagonal is min(M,N) x min(M,N); if M<N, the
*> matrix will be lower bidiagonal, otherwise upper.
*> (c) only tests 5--8 and 14 are performed.
*>
*> A subset of the full set of matrix types may be selected through
*> the logical array DOTYPE.
*> \endverbatim
*
* Arguments:
* ==========
*
*> \param[in] NSIZES
*> \verbatim
*> NSIZES is INTEGER
*> The number of values of M and N contained in the vectors
*> MVAL and NVAL. The matrix sizes are used in pairs (M,N).
*> \endverbatim
*>
*> \param[in] MVAL
*> \verbatim
*> MVAL is INTEGER array, dimension (NM)
*> The values of the matrix row dimension M.
*> \endverbatim
*>
*> \param[in] NVAL
*> \verbatim
*> NVAL is INTEGER array, dimension (NM)
*> The values of the matrix column dimension N.
*> \endverbatim
*>
*> \param[in] NTYPES
*> \verbatim
*> NTYPES is INTEGER
*> The number of elements in DOTYPE. If it is zero, SCHKBD
*> does nothing. It must be at least zero. If it is MAXTYP+1
*> and NSIZES is 1, then an additional type, MAXTYP+1 is
*> defined, which is to use whatever matrices are in A and B.
*> 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 (m,n), a matrix
*> 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] NRHS
*> \verbatim
*> NRHS is INTEGER
*> The number of columns in the "right-hand side" matrices X, Y,
*> and Z, used in testing SBDSQR. If NRHS = 0, then the
*> operations on the right-hand side will not be tested.
*> NRHS must be at least 0.
*> \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 values of ISEED are changed on exit, and can be
*> used in the next call to SCHKBD to continue the same random
*> number sequence.
*> \endverbatim
*>
*> \param[in] THRESH
*> \verbatim
*> THRESH is REAL
*> The threshold value for the test ratios. A result is
*> included in the output file if RESULT >= THRESH. To have
*> every test ratio printed, use THRESH = 0. Note that the
*> expected value of the test ratios is O(1), so THRESH should
*> be a reasonably small multiple of 1, e.g., 10 or 100.
*> \endverbatim
*>
*> \param[out] A
*> \verbatim
*> A is REAL array, dimension (LDA,NMAX)
*> where NMAX is the maximum value of N in NVAL.
*> \endverbatim
*>
*> \param[in] LDA
*> \verbatim
*> LDA is INTEGER
*> The leading dimension of the array A. LDA >= max(1,MMAX),
*> where MMAX is the maximum value of M in MVAL.
*> \endverbatim
*>
*> \param[out] BD
*> \verbatim
*> BD is REAL array, dimension
*> (max(min(MVAL(j),NVAL(j))))
*> \endverbatim
*>
*> \param[out] BE
*> \verbatim
*> BE is REAL array, dimension
*> (max(min(MVAL(j),NVAL(j))))
*> \endverbatim
*>
*> \param[out] S1
*> \verbatim
*> S1 is REAL array, dimension
*> (max(min(MVAL(j),NVAL(j))))
*> \endverbatim
*>
*> \param[out] S2
*> \verbatim
*> S2 is REAL array, dimension
*> (max(min(MVAL(j),NVAL(j))))
*> \endverbatim
*>
*> \param[out] X
*> \verbatim
*> X is REAL array, dimension (LDX,NRHS)
*> \endverbatim
*>
*> \param[in] LDX
*> \verbatim
*> LDX is INTEGER
*> The leading dimension of the arrays X, Y, and Z.
*> LDX >= max(1,MMAX)
*> \endverbatim
*>
*> \param[out] Y
*> \verbatim
*> Y is REAL array, dimension (LDX,NRHS)
*> \endverbatim
*>
*> \param[out] Z
*> \verbatim
*> Z is REAL array, dimension (LDX,NRHS)
*> \endverbatim
*>
*> \param[out] Q
*> \verbatim
*> Q is REAL array, dimension (LDQ,MMAX)
*> \endverbatim
*>
*> \param[in] LDQ
*> \verbatim
*> LDQ is INTEGER
*> The leading dimension of the array Q. LDQ >= max(1,MMAX).
*> \endverbatim
*>
*> \param[out] PT
*> \verbatim
*> PT is REAL array, dimension (LDPT,NMAX)
*> \endverbatim
*>
*> \param[in] LDPT
*> \verbatim
*> LDPT is INTEGER
*> The leading dimension of the arrays PT, U, and V.
*> LDPT >= max(1, max(min(MVAL(j),NVAL(j)))).
*> \endverbatim
*>
*> \param[out] U
*> \verbatim
*> U is REAL array, dimension
*> (LDPT,max(min(MVAL(j),NVAL(j))))
*> \endverbatim
*>
*> \param[out] VT
*> \verbatim
*> VT is REAL array, dimension
*> (LDPT,max(min(MVAL(j),NVAL(j))))
*> \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
*> 3(M+N) and M(M + max(M,N,k) + 1) + N*min(M,N) for all
*> pairs (M,N)=(MM(j),NN(j))
*> \endverbatim
*>
*> \param[out] IWORK
*> \verbatim
*> IWORK is INTEGER array, dimension at least 8*min(M,N)
*> \endverbatim
*>
*> \param[in] NOUT
*> \verbatim
*> NOUT is INTEGER
*> The FORTRAN unit number for printing out error messages
*> (e.g., if a routine returns IINFO not equal to 0.)
*> \endverbatim
*>
*> \param[out] INFO
*> \verbatim
*> INFO is INTEGER
*> If 0, then everything ran OK.
*> -1: NSIZES < 0
*> -2: Some MM(j) < 0
*> -3: Some NN(j) < 0
*> -4: NTYPES < 0
*> -6: NRHS < 0
*> -8: THRESH < 0
*> -11: LDA < 1 or LDA < MMAX, where MMAX is max( MM(j) ).
*> -17: LDB < 1 or LDB < MMAX.
*> -21: LDQ < 1 or LDQ < MMAX.
*> -23: LDPT< 1 or LDPT< MNMAX.
*> -27: LWORK too small.
*> If SLATMR, SLATMS, SGEBRD, SORGBR, or SBDSQR,
*> returns an error code, the
*> absolute value of it is returned.
*>
*>-----------------------------------------------------------------------
*>
*> Some Local Variables and Parameters:
*> ---- ----- --------- --- ----------
*>
*> ZERO, ONE Real 0 and 1.
*> MAXTYP The number of types defined.
*> NTEST The number of tests performed, or which can
*> be performed so far, for the current matrix.
*> MMAX Largest value in NN.
*> NMAX Largest value in NN.
*> MNMIN min(MM(j), NN(j)) (the dimension of the bidiagonal
*> matrix.)
*> MNMAX The maximum value of MNMIN for j=1,...,NSIZES.
*> NFAIL The number of tests which have exceeded THRESH
*> COND, IMODE Values to be passed to the matrix generators.
*> ANORM Norm of A; passed to matrix generators.
*>
*> OVFL, UNFL Overflow and underflow thresholds.
*> RTOVFL, RTUNFL Square roots of the previous 2 values.
*> ULP, ULPINV Finest relative precision and its inverse.
*>
*> 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) )
*> \endverbatim
*
* Authors:
* ========
*
*> \author Univ. of Tennessee
*> \author Univ. of California Berkeley
*> \author Univ. of Colorado Denver
*> \author NAG Ltd.
*
*> \ingroup single_eig
*
* =====================================================================
SUBROUTINE SCHKBD( NSIZES, MVAL, NVAL, NTYPES, DOTYPE, NRHS,
$ ISEED, THRESH, A, LDA, BD, BE, S1, S2, X, LDX,
$ Y, Z, Q, LDQ, PT, LDPT, U, VT, WORK, LWORK,
$ IWORK, NOUT, INFO )
*
* -- LAPACK test routine --
* -- LAPACK is a software package provided by Univ. of Tennessee, --
* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
*
* .. Scalar Arguments ..
INTEGER INFO, LDA, LDPT, LDQ, LDX, LWORK, NOUT, NRHS,
$ NSIZES, NTYPES
REAL THRESH
* ..
* .. Array Arguments ..
LOGICAL DOTYPE( * )
INTEGER ISEED( 4 ), IWORK( * ), MVAL( * ), NVAL( * )
REAL A( LDA, * ), BD( * ), BE( * ), PT( LDPT, * ),
$ Q( LDQ, * ), S1( * ), S2( * ), U( LDPT, * ),
$ VT( LDPT, * ), WORK( * ), X( LDX, * ),
$ Y( LDX, * ), Z( LDX, * )
* ..
*
* ======================================================================
*
* .. Parameters ..
REAL ZERO, ONE, TWO, HALF
PARAMETER ( ZERO = 0.0E0, ONE = 1.0E0, TWO = 2.0E0,
$ HALF = 0.5E0 )
INTEGER MAXTYP
PARAMETER ( MAXTYP = 16 )
* ..
* .. Local Scalars ..
LOGICAL BADMM, BADNN, BIDIAG
CHARACTER UPLO
CHARACTER*3 PATH
INTEGER I, IINFO, IL, IMODE, ITEMP, ITYPE, IU, IWBD,
$ IWBE, IWBS, IWBZ, IWWORK, J, JCOL, JSIZE,
$ JTYPE, LOG2UI, M, MINWRK, MMAX, MNMAX, MNMIN,
$ MNMIN2, MQ, MTYPES, N, NFAIL, NMAX,
$ NS1, NS2, NTEST
REAL ABSTOL, AMNINV, ANORM, COND, OVFL, RTOVFL,
$ RTUNFL, TEMP1, TEMP2, ULP, ULPINV, UNFL,
$ VL, VU
* ..
* .. Local Arrays ..
INTEGER IDUM( 1 ), IOLDSD( 4 ), ISEED2( 4 ),
$ KMAGN( MAXTYP ), KMODE( MAXTYP ),
$ KTYPE( MAXTYP )
REAL DUM( 1 ), DUMMA( 1 ), RESULT( 40 )
* ..
* .. External Functions ..
REAL SLAMCH, SLARND, SSXT1
EXTERNAL SLAMCH, SLARND, SSXT1
* ..
* .. External Subroutines ..
EXTERNAL ALASUM, SBDSDC, SBDSQR, SBDSVDX, SBDT01,
$ SBDT02, SBDT03, SBDT04, SCOPY, SGEBRD,
$ SGEMM, SLACPY, SLAHD2, SLASET, SLATMR,
$ SLATMS, SORGBR, SORT01, XERBLA
* ..
* .. Intrinsic Functions ..
INTRINSIC ABS, EXP, INT, LOG, MAX, MIN, SQRT
* ..
* .. Scalars in Common ..
LOGICAL LERR, OK
CHARACTER*32 SRNAMT
INTEGER INFOT, NUNIT
* ..
* .. Common blocks ..
COMMON / INFOC / INFOT, NUNIT, OK, LERR
COMMON / SRNAMC / SRNAMT
* ..
* .. Data statements ..
DATA KTYPE / 1, 2, 5*4, 5*6, 3*9, 10 /
DATA KMAGN / 2*1, 3*1, 2, 3, 3*1, 2, 3, 1, 2, 3, 0 /
DATA KMODE / 2*0, 4, 3, 1, 4, 4, 4, 3, 1, 4, 4, 0,
$ 0, 0, 0 /
* ..
* .. Executable Statements ..
*
* Check for errors
*
INFO = 0
*
BADMM = .FALSE.
BADNN = .FALSE.
MMAX = 1
NMAX = 1
MNMAX = 1
MINWRK = 1
DO 10 J = 1, NSIZES
MMAX = MAX( MMAX, MVAL( J ) )
IF( MVAL( J ).LT.0 )
$ BADMM = .TRUE.
NMAX = MAX( NMAX, NVAL( J ) )
IF( NVAL( J ).LT.0 )
$ BADNN = .TRUE.
MNMAX = MAX( MNMAX, MIN( MVAL( J ), NVAL( J ) ) )
MINWRK = MAX( MINWRK, 3*( MVAL( J )+NVAL( J ) ),
$ MVAL( J )*( MVAL( J )+MAX( MVAL( J ), NVAL( J ),
$ NRHS )+1 )+NVAL( J )*MIN( NVAL( J ), MVAL( J ) ) )
10 CONTINUE
*
* Check for errors
*
IF( NSIZES.LT.0 ) THEN
INFO = -1
ELSE IF( BADMM ) THEN
INFO = -2
ELSE IF( BADNN ) THEN
INFO = -3
ELSE IF( NTYPES.LT.0 ) THEN
INFO = -4
ELSE IF( NRHS.LT.0 ) THEN
INFO = -6
ELSE IF( LDA.LT.MMAX ) THEN
INFO = -11
ELSE IF( LDX.LT.MMAX ) THEN
INFO = -17
ELSE IF( LDQ.LT.MMAX ) THEN
INFO = -21
ELSE IF( LDPT.LT.MNMAX ) THEN
INFO = -23
ELSE IF( MINWRK.GT.LWORK ) THEN
INFO = -27
END IF
*
IF( INFO.NE.0 ) THEN
CALL XERBLA( 'SCHKBD', -INFO )
RETURN
END IF
*
* Initialize constants
*
PATH( 1: 1 ) = 'Single precision'
PATH( 2: 3 ) = 'BD'
NFAIL = 0
NTEST = 0
UNFL = SLAMCH( 'Safe minimum' )
OVFL = SLAMCH( 'Overflow' )
ULP = SLAMCH( 'Precision' )
ULPINV = ONE / ULP
LOG2UI = INT( LOG( ULPINV ) / LOG( TWO ) )
RTUNFL = SQRT( UNFL )
RTOVFL = SQRT( OVFL )
INFOT = 0
ABSTOL = 2*UNFL
*
* Loop over sizes, types
*
DO 300 JSIZE = 1, NSIZES
M = MVAL( JSIZE )
N = NVAL( JSIZE )
MNMIN = MIN( M, N )
AMNINV = ONE / MAX( M, N, 1 )
*
IF( NSIZES.NE.1 ) THEN
MTYPES = MIN( MAXTYP, NTYPES )
ELSE
MTYPES = MIN( MAXTYP+1, NTYPES )
END IF
*
DO 290 JTYPE = 1, MTYPES
IF( .NOT.DOTYPE( JTYPE ) )
$ GO TO 290
*
DO 20 J = 1, 4
IOLDSD( J ) = ISEED( J )
20 CONTINUE
*
DO 30 J = 1, 34
RESULT( J ) = -ONE
30 CONTINUE
*
UPLO = ' '
*
* Compute "A"
*
* Control parameters:
*
* KMAGN KMODE KTYPE
* =1 O(1) clustered 1 zero
* =2 large clustered 2 identity
* =3 small exponential (none)
* =4 arithmetic diagonal, (w/ eigenvalues)
* =5 random symmetric, w/ eigenvalues
* =6 nonsymmetric, w/ singular values
* =7 random diagonal
* =8 random symmetric
* =9 random nonsymmetric
* =10 random bidiagonal (log. distrib.)
*
IF( MTYPES.GT.MAXTYP )
$ GO TO 100
*
ITYPE = KTYPE( JTYPE )
IMODE = KMODE( JTYPE )
*
* Compute norm
*
GO TO ( 40, 50, 60 )KMAGN( JTYPE )
*
40 CONTINUE
ANORM = ONE
GO TO 70
*
50 CONTINUE
ANORM = ( RTOVFL*ULP )*AMNINV
GO TO 70
*
60 CONTINUE
ANORM = RTUNFL*MAX( M, N )*ULPINV
GO TO 70
*
70 CONTINUE
*
CALL SLASET( 'Full', LDA, N, ZERO, ZERO, A, LDA )
IINFO = 0
COND = ULPINV
*
BIDIAG = .FALSE.
IF( ITYPE.EQ.1 ) THEN
*
* Zero matrix
*
IINFO = 0
*
ELSE IF( ITYPE.EQ.2 ) THEN
*
* Identity
*
DO 80 JCOL = 1, MNMIN
A( JCOL, JCOL ) = ANORM
80 CONTINUE
*
ELSE IF( ITYPE.EQ.4 ) THEN
*
* Diagonal Matrix, [Eigen]values Specified
*
CALL SLATMS( MNMIN, MNMIN, 'S', ISEED, 'N', WORK, IMODE,
$ COND, ANORM, 0, 0, 'N', A, LDA,
$ WORK( MNMIN+1 ), IINFO )
*
ELSE IF( ITYPE.EQ.5 ) THEN
*
* Symmetric, eigenvalues specified
*
CALL SLATMS( MNMIN, MNMIN, 'S', ISEED, 'S', WORK, IMODE,
$ COND, ANORM, M, N, 'N', A, LDA,
$ WORK( MNMIN+1 ), IINFO )
*
ELSE IF( ITYPE.EQ.6 ) THEN
*
* Nonsymmetric, singular values specified
*
CALL SLATMS( M, N, 'S', ISEED, 'N', WORK, IMODE, COND,
$ ANORM, M, N, 'N', A, LDA, WORK( MNMIN+1 ),
$ IINFO )
*
ELSE IF( ITYPE.EQ.7 ) THEN
*
* Diagonal, random entries
*
CALL SLATMR( MNMIN, MNMIN, 'S', ISEED, 'N', WORK, 6, ONE,
$ ONE, 'T', 'N', WORK( MNMIN+1 ), 1, ONE,
$ WORK( 2*MNMIN+1 ), 1, ONE, 'N', IWORK, 0, 0,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE IF( ITYPE.EQ.8 ) THEN
*
* Symmetric, random entries
*
CALL SLATMR( MNMIN, MNMIN, 'S', ISEED, 'S', WORK, 6, ONE,
$ ONE, 'T', 'N', WORK( MNMIN+1 ), 1, ONE,
$ WORK( M+MNMIN+1 ), 1, ONE, 'N', IWORK, M, N,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE IF( ITYPE.EQ.9 ) THEN
*
* Nonsymmetric, random entries
*
CALL SLATMR( M, N, 'S', ISEED, 'N', WORK, 6, ONE, ONE,
$ 'T', 'N', WORK( MNMIN+1 ), 1, ONE,
$ WORK( M+MNMIN+1 ), 1, ONE, 'N', IWORK, M, N,
$ ZERO, ANORM, 'NO', A, LDA, IWORK, IINFO )
*
ELSE IF( ITYPE.EQ.10 ) THEN
*
* Bidiagonal, random entries
*
TEMP1 = -TWO*LOG( ULP )
DO 90 J = 1, MNMIN
BD( J ) = EXP( TEMP1*SLARND( 2, ISEED ) )
IF( J.LT.MNMIN )
$ BE( J ) = EXP( TEMP1*SLARND( 2, ISEED ) )
90 CONTINUE
*
IINFO = 0
BIDIAG = .TRUE.
IF( M.GE.N ) THEN
UPLO = 'U'
ELSE
UPLO = 'L'
END IF
ELSE
IINFO = 1
END IF
*
IF( IINFO.EQ.0 ) THEN
*
* Generate Right-Hand Side
*
IF( BIDIAG ) THEN
CALL SLATMR( MNMIN, NRHS, 'S', ISEED, 'N', WORK, 6,
$ ONE, ONE, 'T', 'N', WORK( MNMIN+1 ), 1,
$ ONE, WORK( 2*MNMIN+1 ), 1, ONE, 'N',
$ IWORK, MNMIN, NRHS, ZERO, ONE, 'NO', Y,
$ LDX, IWORK, IINFO )
ELSE
CALL SLATMR( M, NRHS, 'S', ISEED, 'N', WORK, 6, ONE,
$ ONE, 'T', 'N', WORK( M+1 ), 1, ONE,
$ WORK( 2*M+1 ), 1, ONE, 'N', IWORK, M,
$ NRHS, ZERO, ONE, 'NO', X, LDX, IWORK,
$ IINFO )
END IF
END IF
*
* Error Exit
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'Generator', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
RETURN
END IF
*
100 CONTINUE
*
* Call SGEBRD and SORGBR to compute B, Q, and P, do tests.
*
IF( .NOT.BIDIAG ) THEN
*
* Compute transformations to reduce A to bidiagonal form:
* B := Q' * A * P.
*
CALL SLACPY( ' ', M, N, A, LDA, Q, LDQ )
CALL SGEBRD( M, N, Q, LDQ, BD, BE, WORK, WORK( MNMIN+1 ),
$ WORK( 2*MNMIN+1 ), LWORK-2*MNMIN, IINFO )
*
* Check error code from SGEBRD.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SGEBRD', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
RETURN
END IF
*
CALL SLACPY( ' ', M, N, Q, LDQ, PT, LDPT )
IF( M.GE.N ) THEN
UPLO = 'U'
ELSE
UPLO = 'L'
END IF
*
* Generate Q
*
MQ = M
IF( NRHS.LE.0 )
$ MQ = MNMIN
CALL SORGBR( 'Q', M, MQ, N, Q, LDQ, WORK,
$ WORK( 2*MNMIN+1 ), LWORK-2*MNMIN, IINFO )
*
* Check error code from SORGBR.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SORGBR(Q)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
RETURN
END IF
*
* Generate P'
*
CALL SORGBR( 'P', MNMIN, N, M, PT, LDPT, WORK( MNMIN+1 ),
$ WORK( 2*MNMIN+1 ), LWORK-2*MNMIN, IINFO )
*
* Check error code from SORGBR.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SORGBR(P)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
RETURN
END IF
*
* Apply Q' to an M by NRHS matrix X: Y := Q' * X.
*
CALL SGEMM( 'Transpose', 'No transpose', M, NRHS, M, ONE,
$ Q, LDQ, X, LDX, ZERO, Y, LDX )
*
* Test 1: Check the decomposition A := Q * B * PT
* 2: Check the orthogonality of Q
* 3: Check the orthogonality of PT
*
CALL SBDT01( M, N, 1, A, LDA, Q, LDQ, BD, BE, PT, LDPT,
$ WORK, RESULT( 1 ) )
CALL SORT01( 'Columns', M, MQ, Q, LDQ, WORK, LWORK,
$ RESULT( 2 ) )
CALL SORT01( 'Rows', MNMIN, N, PT, LDPT, WORK, LWORK,
$ RESULT( 3 ) )
END IF
*
* Use SBDSQR to form the SVD of the bidiagonal matrix B:
* B := U * S1 * VT, and compute Z = U' * Y.
*
CALL SCOPY( MNMIN, BD, 1, S1, 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK, 1 )
CALL SLACPY( ' ', M, NRHS, Y, LDX, Z, LDX )
CALL SLASET( 'Full', MNMIN, MNMIN, ZERO, ONE, U, LDPT )
CALL SLASET( 'Full', MNMIN, MNMIN, ZERO, ONE, VT, LDPT )
*
CALL SBDSQR( UPLO, MNMIN, MNMIN, MNMIN, NRHS, S1, WORK, VT,
$ LDPT, U, LDPT, Z, LDX, WORK( MNMIN+1 ), IINFO )
*
* Check error code from SBDSQR.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSQR(vects)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 4 ) = ULPINV
GO TO 270
END IF
END IF
*
* Use SBDSQR to compute only the singular values of the
* bidiagonal matrix B; U, VT, and Z should not be modified.
*
CALL SCOPY( MNMIN, BD, 1, S2, 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK, 1 )
*
CALL SBDSQR( UPLO, MNMIN, 0, 0, 0, S2, WORK, VT, LDPT, U,
$ LDPT, Z, LDX, WORK( MNMIN+1 ), IINFO )
*
* Check error code from SBDSQR.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSQR(values)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 9 ) = ULPINV
GO TO 270
END IF
END IF
*
* Test 4: Check the decomposition B := U * S1 * VT
* 5: Check the computation Z := U' * Y
* 6: Check the orthogonality of U
* 7: Check the orthogonality of VT
*
CALL SBDT03( UPLO, MNMIN, 1, BD, BE, U, LDPT, S1, VT, LDPT,
$ WORK, RESULT( 4 ) )
CALL SBDT02( MNMIN, NRHS, Y, LDX, Z, LDX, U, LDPT, WORK,
$ RESULT( 5 ) )
CALL SORT01( 'Columns', MNMIN, MNMIN, U, LDPT, WORK, LWORK,
$ RESULT( 6 ) )
CALL SORT01( 'Rows', MNMIN, MNMIN, VT, LDPT, WORK, LWORK,
$ RESULT( 7 ) )
*
* Test 8: Check that the singular values are sorted in
* non-increasing order and are non-negative
*
RESULT( 8 ) = ZERO
DO 110 I = 1, MNMIN - 1
IF( S1( I ).LT.S1( I+1 ) )
$ RESULT( 8 ) = ULPINV
IF( S1( I ).LT.ZERO )
$ RESULT( 8 ) = ULPINV
110 CONTINUE
IF( MNMIN.GE.1 ) THEN
IF( S1( MNMIN ).LT.ZERO )
$ RESULT( 8 ) = ULPINV
END IF
*
* Test 9: Compare SBDSQR with and without singular vectors
*
TEMP2 = ZERO
*
DO 120 J = 1, MNMIN
TEMP1 = ABS( S1( J )-S2( J ) ) /
$ MAX( SQRT( UNFL )*MAX( S1( 1 ), ONE ),
$ ULP*MAX( ABS( S1( J ) ), ABS( S2( J ) ) ) )
TEMP2 = MAX( TEMP1, TEMP2 )
120 CONTINUE
*
RESULT( 9 ) = TEMP2
*
* Test 10: Sturm sequence test of singular values
* Go up by factors of two until it succeeds
*
TEMP1 = THRESH*( HALF-ULP )
*
DO 130 J = 0, LOG2UI
* CALL SSVDCH( MNMIN, BD, BE, S1, TEMP1, IINFO )
IF( IINFO.EQ.0 )
$ GO TO 140
TEMP1 = TEMP1*TWO
130 CONTINUE
*
140 CONTINUE
RESULT( 10 ) = TEMP1
*
* Use SBDSQR to form the decomposition A := (QU) S (VT PT)
* from the bidiagonal form A := Q B PT.
*
IF( .NOT.BIDIAG ) THEN
CALL SCOPY( MNMIN, BD, 1, S2, 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK, 1 )
*
CALL SBDSQR( UPLO, MNMIN, N, M, NRHS, S2, WORK, PT, LDPT,
$ Q, LDQ, Y, LDX, WORK( MNMIN+1 ), IINFO )
*
* Test 11: Check the decomposition A := Q*U * S2 * VT*PT
* 12: Check the computation Z := U' * Q' * X
* 13: Check the orthogonality of Q*U
* 14: Check the orthogonality of VT*PT
*
CALL SBDT01( M, N, 0, A, LDA, Q, LDQ, S2, DUMMA, PT,
$ LDPT, WORK, RESULT( 11 ) )
CALL SBDT02( M, NRHS, X, LDX, Y, LDX, Q, LDQ, WORK,
$ RESULT( 12 ) )
CALL SORT01( 'Columns', M, MQ, Q, LDQ, WORK, LWORK,
$ RESULT( 13 ) )
CALL SORT01( 'Rows', MNMIN, N, PT, LDPT, WORK, LWORK,
$ RESULT( 14 ) )
END IF
*
* Use SBDSDC to form the SVD of the bidiagonal matrix B:
* B := U * S1 * VT
*
CALL SCOPY( MNMIN, BD, 1, S1, 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK, 1 )
CALL SLASET( 'Full', MNMIN, MNMIN, ZERO, ONE, U, LDPT )
CALL SLASET( 'Full', MNMIN, MNMIN, ZERO, ONE, VT, LDPT )
*
CALL SBDSDC( UPLO, 'I', MNMIN, S1, WORK, U, LDPT, VT, LDPT,
$ DUM, IDUM, WORK( MNMIN+1 ), IWORK, IINFO )
*
* Check error code from SBDSDC.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSDC(vects)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 15 ) = ULPINV
GO TO 270
END IF
END IF
*
* Use SBDSDC to compute only the singular values of the
* bidiagonal matrix B; U and VT should not be modified.
*
CALL SCOPY( MNMIN, BD, 1, S2, 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK, 1 )
*
CALL SBDSDC( UPLO, 'N', MNMIN, S2, WORK, DUM, 1, DUM, 1,
$ DUM, IDUM, WORK( MNMIN+1 ), IWORK, IINFO )
*
* Check error code from SBDSDC.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSDC(values)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 18 ) = ULPINV
GO TO 270
END IF
END IF
*
* Test 15: Check the decomposition B := U * S1 * VT
* 16: Check the orthogonality of U
* 17: Check the orthogonality of VT
*
CALL SBDT03( UPLO, MNMIN, 1, BD, BE, U, LDPT, S1, VT, LDPT,
$ WORK, RESULT( 15 ) )
CALL SORT01( 'Columns', MNMIN, MNMIN, U, LDPT, WORK, LWORK,
$ RESULT( 16 ) )
CALL SORT01( 'Rows', MNMIN, MNMIN, VT, LDPT, WORK, LWORK,
$ RESULT( 17 ) )
*
* Test 18: Check that the singular values are sorted in
* non-increasing order and are non-negative
*
RESULT( 18 ) = ZERO
DO 150 I = 1, MNMIN - 1
IF( S1( I ).LT.S1( I+1 ) )
$ RESULT( 18 ) = ULPINV
IF( S1( I ).LT.ZERO )
$ RESULT( 18 ) = ULPINV
150 CONTINUE
IF( MNMIN.GE.1 ) THEN
IF( S1( MNMIN ).LT.ZERO )
$ RESULT( 18 ) = ULPINV
END IF
*
* Test 19: Compare SBDSQR with and without singular vectors
*
TEMP2 = ZERO
*
DO 160 J = 1, MNMIN
TEMP1 = ABS( S1( J )-S2( J ) ) /
$ MAX( SQRT( UNFL )*MAX( S1( 1 ), ONE ),
$ ULP*MAX( ABS( S1( 1 ) ), ABS( S2( 1 ) ) ) )
TEMP2 = MAX( TEMP1, TEMP2 )
160 CONTINUE
*
RESULT( 19 ) = TEMP2
*
*
* Use SBDSVDX to compute the SVD of the bidiagonal matrix B:
* B := U * S1 * VT
*
IF( JTYPE.EQ.10 .OR. JTYPE.EQ.16 ) THEN
* =================================
* Matrix types temporarily disabled
* =================================
RESULT( 20:34 ) = ZERO
GO TO 270
END IF
*
IWBS = 1
IWBD = IWBS + MNMIN
IWBE = IWBD + MNMIN
IWBZ = IWBE + MNMIN
IWWORK = IWBZ + 2*MNMIN*(MNMIN+1)
MNMIN2 = MAX( 1,MNMIN*2 )
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'V', 'A', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), ZERO, ZERO, 0, 0, NS1, S1,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO)
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(vects,A)', IINFO, M, N,
$ JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 20 ) = ULPINV
GO TO 270
END IF
END IF
*
J = IWBZ
DO 170 I = 1, NS1
CALL SCOPY( MNMIN, WORK( J ), 1, U( 1,I ), 1 )
J = J + MNMIN
CALL SCOPY( MNMIN, WORK( J ), 1, VT( I,1 ), LDPT )
J = J + MNMIN
170 CONTINUE
*
* Use SBDSVDX to compute only the singular values of the
* bidiagonal matrix B; U and VT should not be modified.
*
IF( JTYPE.EQ.9 ) THEN
* =================================
* Matrix types temporarily disabled
* =================================
RESULT( 24 ) = ZERO
GO TO 270
END IF
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'N', 'A', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), ZERO, ZERO, 0, 0, NS2, S2,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO )
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(values,A)', IINFO,
$ M, N, JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 24 ) = ULPINV
GO TO 270
END IF
END IF
*
* Save S1 for tests 30-34.
*
CALL SCOPY( MNMIN, S1, 1, WORK( IWBS ), 1 )
*
* Test 20: Check the decomposition B := U * S1 * VT
* 21: Check the orthogonality of U
* 22: Check the orthogonality of VT
* 23: Check that the singular values are sorted in
* non-increasing order and are non-negative
* 24: Compare SBDSVDX with and without singular vectors
*
CALL SBDT03( UPLO, MNMIN, 1, BD, BE, U, LDPT, S1, VT,
$ LDPT, WORK( IWBS+MNMIN ), RESULT( 20 ) )
CALL SORT01( 'Columns', MNMIN, MNMIN, U, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 21 ) )
CALL SORT01( 'Rows', MNMIN, MNMIN, VT, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 22) )
*
RESULT( 23 ) = ZERO
DO 180 I = 1, MNMIN - 1
IF( S1( I ).LT.S1( I+1 ) )
$ RESULT( 23 ) = ULPINV
IF( S1( I ).LT.ZERO )
$ RESULT( 23 ) = ULPINV
180 CONTINUE
IF( MNMIN.GE.1 ) THEN
IF( S1( MNMIN ).LT.ZERO )
$ RESULT( 23 ) = ULPINV
END IF
*
TEMP2 = ZERO
DO 190 J = 1, MNMIN
TEMP1 = ABS( S1( J )-S2( J ) ) /
$ MAX( SQRT( UNFL )*MAX( S1( 1 ), ONE ),
$ ULP*MAX( ABS( S1( 1 ) ), ABS( S2( 1 ) ) ) )
TEMP2 = MAX( TEMP1, TEMP2 )
190 CONTINUE
RESULT( 24 ) = TEMP2
ANORM = S1( 1 )
*
* Use SBDSVDX with RANGE='I': choose random values for IL and
* IU, and ask for the IL-th through IU-th singular values
* and corresponding vectors.
*
DO 200 I = 1, 4
ISEED2( I ) = ISEED( I )
200 CONTINUE
IF( MNMIN.LE.1 ) THEN
IL = 1
IU = MNMIN
ELSE
IL = 1 + INT( ( MNMIN-1 )*SLARND( 1, ISEED2 ) )
IU = 1 + INT( ( MNMIN-1 )*SLARND( 1, ISEED2 ) )
IF( IU.LT.IL ) THEN
ITEMP = IU
IU = IL
IL = ITEMP
END IF
END IF
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'V', 'I', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), ZERO, ZERO, IL, IU, NS1, S1,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO)
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(vects,I)', IINFO,
$ M, N, JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 25 ) = ULPINV
GO TO 270
END IF
END IF
*
J = IWBZ
DO 210 I = 1, NS1
CALL SCOPY( MNMIN, WORK( J ), 1, U( 1,I ), 1 )
J = J + MNMIN
CALL SCOPY( MNMIN, WORK( J ), 1, VT( I,1 ), LDPT )
J = J + MNMIN
210 CONTINUE
*
* Use SBDSVDX to compute only the singular values of the
* bidiagonal matrix B; U and VT should not be modified.
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'N', 'I', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), ZERO, ZERO, IL, IU, NS2, S2,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO )
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(values,I)', IINFO,
$ M, N, JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 29 ) = ULPINV
GO TO 270
END IF
END IF
*
* Test 25: Check S1 - U' * B * VT'
* 26: Check the orthogonality of U
* 27: Check the orthogonality of VT
* 28: Check that the singular values are sorted in
* non-increasing order and are non-negative
* 29: Compare SBDSVDX with and without singular vectors
*
CALL SBDT04( UPLO, MNMIN, BD, BE, S1, NS1, U,
$ LDPT, VT, LDPT, WORK( IWBS+MNMIN ),
$ RESULT( 25 ) )
CALL SORT01( 'Columns', MNMIN, NS1, U, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 26 ) )
CALL SORT01( 'Rows', NS1, MNMIN, VT, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 27 ) )
*
RESULT( 28 ) = ZERO
DO 220 I = 1, NS1 - 1
IF( S1( I ).LT.S1( I+1 ) )
$ RESULT( 28 ) = ULPINV
IF( S1( I ).LT.ZERO )
$ RESULT( 28 ) = ULPINV
220 CONTINUE
IF( NS1.GE.1 ) THEN
IF( S1( NS1 ).LT.ZERO )
$ RESULT( 28 ) = ULPINV
END IF
*
TEMP2 = ZERO
DO 230 J = 1, NS1
TEMP1 = ABS( S1( J )-S2( J ) ) /
$ MAX( SQRT( UNFL )*MAX( S1( 1 ), ONE ),
$ ULP*MAX( ABS( S1( 1 ) ), ABS( S2( 1 ) ) ) )
TEMP2 = MAX( TEMP1, TEMP2 )
230 CONTINUE
RESULT( 29 ) = TEMP2
*
* Use SBDSVDX with RANGE='V': determine the values VL and VU
* of the IL-th and IU-th singular values and ask for all
* singular values in this range.
*
CALL SCOPY( MNMIN, WORK( IWBS ), 1, S1, 1 )
*
IF( MNMIN.GT.0 ) THEN
IF( IL.NE.1 ) THEN
VU = S1( IL ) + MAX( HALF*ABS( S1( IL )-S1( IL-1 ) ),
$ ULP*ANORM, TWO*RTUNFL )
ELSE
VU = S1( 1 ) + MAX( HALF*ABS( S1( MNMIN )-S1( 1 ) ),
$ ULP*ANORM, TWO*RTUNFL )
END IF
IF( IU.NE.NS1 ) THEN
VL = S1( IU ) - MAX( ULP*ANORM, TWO*RTUNFL,
$ HALF*ABS( S1( IU+1 )-S1( IU ) ) )
ELSE
VL = S1( NS1 ) - MAX( ULP*ANORM, TWO*RTUNFL,
$ HALF*ABS( S1( MNMIN )-S1( 1 ) ) )
END IF
VL = MAX( VL,ZERO )
VU = MAX( VU,ZERO )
IF( VL.GE.VU ) VU = MAX( VU*2, VU+VL+HALF )
ELSE
VL = ZERO
VU = ONE
END IF
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'V', 'V', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), VL, VU, 0, 0, NS1, S1,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO )
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(vects,V)', IINFO,
$ M, N, JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 30 ) = ULPINV
GO TO 270
END IF
END IF
*
J = IWBZ
DO 240 I = 1, NS1
CALL SCOPY( MNMIN, WORK( J ), 1, U( 1,I ), 1 )
J = J + MNMIN
CALL SCOPY( MNMIN, WORK( J ), 1, VT( I,1 ), LDPT )
J = J + MNMIN
240 CONTINUE
*
* Use SBDSVDX to compute only the singular values of the
* bidiagonal matrix B; U and VT should not be modified.
*
CALL SCOPY( MNMIN, BD, 1, WORK( IWBD ), 1 )
IF( MNMIN.GT.0 )
$ CALL SCOPY( MNMIN-1, BE, 1, WORK( IWBE ), 1 )
*
CALL SBDSVDX( UPLO, 'N', 'V', MNMIN, WORK( IWBD ),
$ WORK( IWBE ), VL, VU, 0, 0, NS2, S2,
$ WORK( IWBZ ), MNMIN2, WORK( IWWORK ),
$ IWORK, IINFO )
*
* Check error code from SBDSVDX.
*
IF( IINFO.NE.0 ) THEN
WRITE( NOUT, FMT = 9998 )'SBDSVDX(values,V)', IINFO,
$ M, N, JTYPE, IOLDSD
INFO = ABS( IINFO )
IF( IINFO.LT.0 ) THEN
RETURN
ELSE
RESULT( 34 ) = ULPINV
GO TO 270
END IF
END IF
*
* Test 30: Check S1 - U' * B * VT'
* 31: Check the orthogonality of U
* 32: Check the orthogonality of VT
* 33: Check that the singular values are sorted in
* non-increasing order and are non-negative
* 34: Compare SBDSVDX with and without singular vectors
*
CALL SBDT04( UPLO, MNMIN, BD, BE, S1, NS1, U,
$ LDPT, VT, LDPT, WORK( IWBS+MNMIN ),
$ RESULT( 30 ) )
CALL SORT01( 'Columns', MNMIN, NS1, U, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 31 ) )
CALL SORT01( 'Rows', NS1, MNMIN, VT, LDPT,
$ WORK( IWBS+MNMIN ), LWORK-MNMIN,
$ RESULT( 32 ) )
*
RESULT( 33 ) = ZERO
DO 250 I = 1, NS1 - 1
IF( S1( I ).LT.S1( I+1 ) )
$ RESULT( 28 ) = ULPINV
IF( S1( I ).LT.ZERO )
$ RESULT( 28 ) = ULPINV
250 CONTINUE
IF( NS1.GE.1 ) THEN
IF( S1( NS1 ).LT.ZERO )
$ RESULT( 28 ) = ULPINV
END IF
*
TEMP2 = ZERO
DO 260 J = 1, NS1
TEMP1 = ABS( S1( J )-S2( J ) ) /
$ MAX( SQRT( UNFL )*MAX( S1( 1 ), ONE ),
$ ULP*MAX( ABS( S1( 1 ) ), ABS( S2( 1 ) ) ) )
TEMP2 = MAX( TEMP1, TEMP2 )
260 CONTINUE
RESULT( 34 ) = TEMP2
*
* End of Loop -- Check for RESULT(j) > THRESH
*
270 CONTINUE
*
DO 280 J = 1, 34
IF( RESULT( J ).GE.THRESH ) THEN
IF( NFAIL.EQ.0 )
$ CALL SLAHD2( NOUT, PATH )
WRITE( NOUT, FMT = 9999 )M, N, JTYPE, IOLDSD, J,
$ RESULT( J )
NFAIL = NFAIL + 1
END IF
280 CONTINUE
IF( .NOT.BIDIAG ) THEN
NTEST = NTEST + 34
ELSE
NTEST = NTEST + 30
END IF
*
290 CONTINUE
300 CONTINUE
*
* Summary
*
CALL ALASUM( PATH, NOUT, NFAIL, NTEST, 0 )
*
RETURN
*
* End of SCHKBD
*
9999 FORMAT( ' M=', I5, ', N=', I5, ', type ', I2, ', seed=',
$ 4( I4, ',' ), ' test(', I2, ')=', G11.4 )
9998 FORMAT( ' SCHKBD: ', A, ' returned INFO=', I6, '.', / 9X, 'M=',
$ I6, ', N=', I6, ', JTYPE=', I6, ', ISEED=(', 3( I5, ',' ),
$ I5, ')' )
*
END