SRC/dlalsd.f(3)

πŸŽ€ SRC/dlalsd.f(3) πŸŽ€

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πŸ’– NAME πŸ’–

SRC/dlalsd.f

✨ SYNOPSIS ✨

🧚 Functions/Subroutines 🧚

subroutine dlalsd (uplo, smlsiz, n, nrhs, d, e, b, ldb, rcond, rank,
           work, iwork, info)

DLALSD uses the singular value decomposition of A to solve the least squares problem. 🫢

πŸ¦‹ Function/Subroutine Documentation πŸ¦‹

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subroutine dlalsd (character uplo, integer smlsiz, integer n, integer nrhs,
       double precision, dimension( * ) d, double precision, dimension( * ) e,
       double precision, dimension( ldb, * ) b, integer ldb, double precision
       rcond, integer rank, double precision, dimension( * ) work, integer,
       dimension( * ) iwork, integer info)

DLALSD uses the singular value decomposition of A to solve the least squares problem. πŸ¦‹

πŸ’« Purpose πŸ’«

okay so 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. πŸ’–

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. 🌸

πŸ“ Parameters πŸ“

  • UPLO β€” UPLO is CHARACTER*1 πŸ’…
    • = 'U': D and E define an upper bidiagonal matrix.
    • = 'L': D and E define a lower bidiagonal matrix.
  • SMLSIZ β€” SMLSIZ is INTEGER. The maximum size of the subproblems at the bottom of the computation tree. 🌈
  • N β€” N is INTEGER. The dimension of the bidiagonal matrix. N >= 0. ✨
  • NRHS β€” NRHS is INTEGER. The number of columns of B. NRHS must be at least 1. πŸŽ€
  • D β€” 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. πŸ’œ
  • E β€” E is DOUBLE PRECISION array, dimension (N-1). Contains the super-diagonal entries of the bidiagonal matrix. On exit, E has been destroyed. 😭
  • B β€” 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. 🫢
  • LDB β€” LDB is INTEGER. The leading dimension of B in the calling subprogram. LDB must be at least max(1,N). ⭐
  • RCOND β€” 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 RCOND*max(S), and X(i) = 0 if S(i) is less than or equal to RCOND*max(S). πŸ”₯
  • RANK β€” RANK is INTEGER. The number of singular values of A greater than RCOND times the largest singular value. πŸ‘‘
  • WORK β€” WORK is DOUBLE PRECISION array, dimension at least (9*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2), where NLVL = max(0, INT(log_2 (N/(SMLSIZ+1))) + 1). 🧚
  • IWORK β€” IWORK is INTEGER array, dimension at least (3*N*NLVL + 11*N). πŸ¦‹
  • INFO β€” INFO is INTEGER πŸ’€
    • = 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).

πŸ‘‘ Author πŸ‘‘

  • Univ. of Tennessee
  • Univ. of California Berkeley
  • Univ. of Colorado Denver
  • NAG Ltd.

🌟 Contributors 🌟

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

Definition at line 171 of file dlalsd.f. πŸ“Œ


🌈 Author 🌈

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