sgelsd - norm solution to a real linear least squares problem
SUBROUTINE SGELSD(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER IWORK(*) REAL RCOND REAL A(LDA,*), B(LDB,*), S(*), WORK(*) SUBROUTINE SGELSD_64(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER*8 IWORK(*) REAL RCOND REAL A(LDA,*), B(LDB,*), S(*), WORK(*) F95 INTERFACE SUBROUTINE GELSD(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER, DIMENSION(:) :: IWORK REAL :: RCOND REAL, DIMENSION(:) :: S, WORK REAL, DIMENSION(:,:) :: A, B SUBROUTINE GELSD_64(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER(8) :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER(8), DIMENSION(:) :: IWORK REAL :: RCOND REAL, DIMENSION(:) :: S, WORK REAL, DIMENSION(:,:) :: A, B C INTERFACE #include <sunperf.h> void sgelsd(int m, int n, int nrhs, float *a, int lda, float *b, int ldb, float *s, float rcond, int *rank, int *info); void sgelsd_64(long m, long n, long nrhs, float *a, long lda, float *b, long ldb, float *s, float rcond, long *rank, long *info);
Oracle Solaris Studio Performance Library sgelsd(3P) NAME sgelsd - compute the minimum-norm solution to a real linear least squares problem SYNOPSIS SUBROUTINE SGELSD(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER IWORK(*) REAL RCOND REAL A(LDA,*), B(LDB,*), S(*), WORK(*) SUBROUTINE SGELSD_64(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER*8 IWORK(*) REAL RCOND REAL A(LDA,*), B(LDB,*), S(*), WORK(*) F95 INTERFACE SUBROUTINE GELSD(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER, DIMENSION(:) :: IWORK REAL :: RCOND REAL, DIMENSION(:) :: S, WORK REAL, DIMENSION(:,:) :: A, B SUBROUTINE GELSD_64(M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, IWORK, INFO) INTEGER(8) :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER(8), DIMENSION(:) :: IWORK REAL :: RCOND REAL, DIMENSION(:) :: S, WORK REAL, DIMENSION(:,:) :: A, B C INTERFACE #include <sunperf.h> void sgelsd(int m, int n, int nrhs, float *a, int lda, float *b, int ldb, float *s, float rcond, int *rank, int *info); void sgelsd_64(long m, long n, long nrhs, float *a, long lda, float *b, long ldb, float *s, float rcond, long *rank, long *info); PURPOSE sgelsd computes the minimum-norm solution to a real linear least squares problem: minimize 2-norm(| b - A*x |) using the singular value decomposition (SVD) of A. A is an M-by-N matrix which may be rank-deficient. Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. The problem is solved in three steps: (1) Reduce the coefficient matrix A to bidiagonal form with Householder transformations, reducing the original problem into a "bidiagonal least squares problem" (BLS) (2) Solve the BLS using a divide and conquer approach. (3) Apply back all the Householder tranformations to solve the original least squares problem. The effective rank of A is determined by treating as zero those singu- lar values which are less than RCOND times the largest singular value. The divide and conquer algorithm 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 X-MP, Cray Y-MP, Cray C-90, or Cray-2. It could conceivably fail on hexadecimal or decimal machines without guard dig- its, but we know of none. ARGUMENTS M (input) The number of rows of A. M >= 0. N (input) The number of columns of A. N >= 0. NRHS (input) The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0. A (input/output) On entry, the M-by-N matrix A. On exit, A has been destroyed. LDA (input) The leading dimension of the array A. LDA >= max(1,M). B (input/output) On entry, the M-by-NRHS right hand side matrix B. On exit, B is overwritten by the N-by-NRHS solution matrix X. If m >= n and RANK = n, the residual sum-of-squares for the solution in the i-th column is given by the sum of squares of elements n+1:m in that column. LDB (input) The leading dimension of the array B. LDB >= max(1,max(M,N)). S (output) The singular values of A in decreasing order. The condition number of A in the 2-norm = S(1)/S(min(m,n)). RCOND (input) RCOND is used to determine the effective rank of A. Singular values S(i) <= RCOND*S(1) are treated as zero. If RCOND < 0, machine precision is used instead. RANK (output) The effective rank of A, i.e., the number of singular values which are greater than RCOND*S(1). WORK (workspace) On exit, if INFO = 0, WORK(1) returns the optimal LWORK. LWORK (input) The dimension of the array WORK. LWORK >= 1. The exact mini- mum amount of workspace needed depends on M, N and NRHS. As long as LWORK is at least N**2 + 13*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2, if M is greater than or equal to N or M**2 + 13*M + 2*M*SMLSIZ + 8*M*NLVL + M*NRHS + (SML- SIZ+1)**2, if M is less than N, the code will execute cor- rectly. SMLSIZ is returned by ILAENV and is equal to the maximum size of the subproblems at the bottom of the computa- tion tree (usually about 25), and NLVL = INT( LOG_2( MIN( M,N )/(SMLSIZ+1) ) ) + 1 For good performance, LWORK should gen- erally be larger. If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA. IWORK (workspace) LIWORK >= 3 * MINMN * NLVL + 11 * MINMN, where MINMN = MIN( M,N ). INFO (output) = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value. > 0: the algorithm for computing the SVD failed to converge; if INFO = i, i off-diagonal elements of an intermediate bidi- agonal form did not converge to zero. FURTHER DETAILS Based on contributions by Ming Gu and Ren-Cang Li, Computer Science Division, University of California at Berkeley, USA Osni Marques, LBNL/NERSC, USA 7 Nov 2015 sgelsd(3P)