SLALSD(3)
π SLALSD(3) β Library Functions Manual π
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π NAME π
The file this page is about is:
/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/slalsd.f
β¨ SYNOPSIS β¨
Functions/Subroutines
subroutine SLALSD (uplo, smlsiz, n, nrhs, d, e, b, ldb, rcond, rank,
work, iwork, info)
SLALSD uses the singular value decomposition of A to solve the least
squares problem.
πΈ Function/Subroutine Documentation πΈ
subroutine SLALSD (...)
subroutine SLALSD (character uplo, integer smlsiz, integer n, integer nrhs,
real, dimension( * ) d, real, dimension( * ) e, real, dimension( ldb, *
) b, integer ldb, real rcond, integer rank, real, dimension( * ) work,
integer, dimension( * ) iwork, integer info)
SLALSD uses the singular value decomposition of A to solve the least squares problem, periodt.
π Purpose π
okay so the whole slay here: SLALSD 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, no cap π
.
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.
π Parameters π
UPLOβUPLOisCHARACTER*1.= 'U':DandEdefine an upper bidiagonal matrix.= 'L':DandEdefine a lower bidiagonal matrix.
SMLSIZβSMLSIZisINTEGER. The maximum size of the subproblems at the bottom of the computation tree.NβNisINTEGER. The dimension of the bidiagonal matrix.N >= 0.NRHSβNRHSisINTEGER. The number of columns ofB.NRHSmust be at least1.DβDisREALarray, dimension(N). On entryDcontains the main diagonal of the bidiagonal matrix. On exit, ifINFO = 0,Dcontains its singular values.EβEisREALarray, dimension(N-1). Contains the super-diagonal entries of the bidiagonal matrix. On exit,Ehas been destroyed.BβBisREALarray, dimension(LDB,NRHS). On input,Bcontains the right hand sides of the least squares problem. On output,Bcontains the solutionX.LDBβLDBisINTEGER. The leading dimension ofBin the calling subprogram.LDBmust be at leastmax(1,N).RCONDβRCONDisREAL. The singular values ofAless than or equal toRCONDtimes the largest singular value are treated as zero in solving the least squares problem. IfRCONDis negative, machine precision is used instead. For example, ifdiag(S)*X=Bwere the least squares problem, wherediag(S)is a diagonal matrix of singular values, the solution would beX(i) = B(i) / S(i)ifS(i)is greater thanRCOND*max(S), andX(i) = 0ifS(i)is less than or equal toRCOND*max(S).RANKβRANKisINTEGER. The number of singular values ofAgreater thanRCONDtimes the largest singular value.WORKβWORKisREALarray, dimension at least(9*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2), whereNLVL = max(0, INT(log_2 (N/(SMLSIZ+1))) + 1).IWORKβIWORKisINTEGERarray, dimension at least(3*N*NLVL + 11*N).INFOβINFOisINTEGER.= 0:successful exit.< 0:ifINFO = -i, thei-th argument had an illegal value.> 0:The algorithm failed to compute a singular value while working on the submatrix lying in rows and columnsINFO/(N+1)throughMOD(INFO,N+1).
π Author π
- Univ. of Tennessee
- Univ. of California Berkeley
- Univ. of Colorado Denver
- NAG Ltd.
π¦ Contributors π¦
- 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 slalsd.f.
π§ Author π§
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