ESMF_DLALSD Subroutine

subroutine ESMF_DLALSD(UPLO, SMLSIZ, N, NRHS, D, E, B, LDB, RCOND, RANK, WORK, IWORK, INFO)

\brief \b ESMF_DLALSD \htmlonly Download ESMF_DLALSD + dependencies [TGZ] [ZIP] [TXT] \endhtmlonly \par Purpose:

\verbatim

ESMF_DLALSD uses the singular value decomposition of A to solve the least squares problem of finding X to minimize the Euclidean norm of each column of A*X-B, where A is N-by-N upper bidiagonal, and X and B are N-by-NRHS. The solution X overwrites B.

The singular values of A smaller than RCOND times the largest singular value are treated as zero in solving the least squares problem; in this case a minimum norm solution is returned. The actual singular values are returned in D in ascending order.

This code makes very mild assumptions about floating point arithmetic. It will work on machines with a guard digit in add/subtract, or on those binary machines without guard digits which subtract like the Cray XMP, Cray YMP, Cray C 90, or Cray 2. It could conceivably fail on hexadecimal or decimal machines without guard digits, but we know of none. \endverbatim \param[in] UPLO \verbatim UPLO is CHARACTER*1 = ‘U’: D and E define an upper bidiagonal matrix. = ‘L’: D and E define a lower bidiagonal matrix. \endverbatim

\param[in] SMLSIZ \verbatim SMLSIZ is INTEGER The maximum size of the subproblems at the bottom of the computation tree. \endverbatim

\param[in] N \verbatim N is INTEGER The dimension of the bidiagonal matrix. N >= 0. \endverbatim

\param[in] NRHS \verbatim NRHS is INTEGER The number of columns of B. NRHS must be at least 1. \endverbatim

\param[in,out] D \verbatim D is DOUBLE PRECISION array, dimension (N) On entry D contains the main diagonal of the bidiagonal matrix. On exit, if INFO = 0, D contains its singular values. \endverbatim

\param[in,out] E \verbatim E is DOUBLE PRECISION array, dimension (N-1) Contains the super-diagonal entries of the bidiagonal matrix. On exit, E has been destroyed. \endverbatim

\param[in,out] B \verbatim B is DOUBLE PRECISION array, dimension (LDB,NRHS) On input, B contains the right hand sides of the least squares problem. On output, B contains the solution X. \endverbatim

\param[in] LDB \verbatim LDB is INTEGER The leading dimension of B in the calling subprogram. LDB must be at least max(1,N). \endverbatim

\param[in] RCOND \verbatim RCOND is DOUBLE PRECISION The singular values of A less than or equal to RCOND times the largest singular value are treated as zero in solving the least squares problem. If RCOND is negative, machine precision is used instead. For example, if diag(S)X=B were the least squares problem, where diag(S) is a diagonal matrix of singular values, the solution would be X(i) = B(i) / S(i) if S(i) is greater than RCONDmax(S), and X(i) = 0 if S(i) is less than or equal to RCOND*max(S). \endverbatim

\param[out] RANK \verbatim RANK is INTEGER The number of singular values of A greater than RCOND times the largest singular value. \endverbatim

\param[out] WORK \verbatim WORK is DOUBLE PRECISION array, dimension at least (9N + 2NSMLSIZ + 8NNLVL + NNRHS + (SMLSIZ+1)**2), where NLVL = max(0, INT(log_2 (N/(SMLSIZ+1))) + 1). \endverbatim

\param[out] IWORK \verbatim IWORK is INTEGER array, dimension at least (3NNLVL + 11*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: The algorithm failed to compute a singular value while working on the submatrix lying in rows and columns INFO/(N+1) through MOD(INFO,N+1). \endverbatim \author Univ. of Tennessee \author Univ. of California Berkeley \author Univ. of Colorado Denver \author NAG Ltd. \date November 2011 \ingroup doubleOTHERcomputational \par Contributors:

Ming Gu and Ren-Cang Li, Computer Science Division, University of
  California at Berkeley, USA \n
Osni Marques, LBNL/NERSC, USA \n

Arguments