la_porpvgrw - la_porpvgrw: reciprocal pivot growth

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realfunctioncla_porpvgrw(character*1uplo,integerncols,complex,dimension(lda,*)a,integerlda,complex,dimension(ldaf,*)af,integerldaf,real,dimension(*)work)CLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. Purpose: CLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is used. If this is much less than 1, the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, estimated condition numbers, and error bounds could be unreliable. ParametersUPLO UPLO is CHARACTER*1 = 'U': Upper triangle of A is stored; = 'L': Lower triangle of A is stored. NCOLS NCOLS is INTEGER The number of columns of the matrix A. NCOLS >= 0. A A is COMPLEX array, dimension (LDA,N) On entry, the N-by-N matrix A. LDA LDA is INTEGER The leading dimension of the array A. LDA >= max(1,N). AF AF is COMPLEX array, dimension (LDAF,N) The triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T, as computed by CPOTRF. LDAF LDAF is INTEGER The leading dimension of the array AF. LDAF >= max(1,N). WORK WORK is REAL array, dimension (2*N) Author Univ. of Tennessee Univ. of California Berkeley Univ. of Colorado Denver NAG Ltd. doubleprecisionfunctiondla_porpvgrw(character*1uplo,integerncols,doubleprecision,dimension(lda,*)a,integerlda,doubleprecision,dimension(ldaf,*)af,integerldaf,doubleprecision,dimension(*)work)DLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. Purpose: DLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is used. If this is much less than 1, the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, estimated condition numbers, and error bounds could be unreliable. ParametersUPLO UPLO is CHARACTER*1 = 'U': Upper triangle of A is stored; = 'L': Lower triangle of A is stored. NCOLS NCOLS is INTEGER The number of columns of the matrix A. NCOLS >= 0. A A is DOUBLE PRECISION array, dimension (LDA,N) On entry, the N-by-N matrix A. LDA LDA is INTEGER The leading dimension of the array A. LDA >= max(1,N). AF AF is DOUBLE PRECISION array, dimension (LDAF,N) The triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T, as computed by DPOTRF. LDAF LDAF is INTEGER The leading dimension of the array AF. LDAF >= max(1,N). WORK WORK is DOUBLE PRECISION array, dimension (2*N) Author Univ. of Tennessee Univ. of California Berkeley Univ. of Colorado Denver NAG Ltd. realfunctionsla_porpvgrw(character*1uplo,integerncols,real,dimension(lda,*)a,integerlda,real,dimension(ldaf,*)af,integerldaf,real,dimension(*)work)SLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. Purpose: SLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is used. If this is much less than 1, the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, estimated condition numbers, and error bounds could be unreliable. ParametersUPLO UPLO is CHARACTER*1 = 'U': Upper triangle of A is stored; = 'L': Lower triangle of A is stored. NCOLS NCOLS is INTEGER The number of columns of the matrix A. NCOLS >= 0. A A is REAL array, dimension (LDA,N) On entry, the N-by-N matrix A. LDA LDA is INTEGER The leading dimension of the array A. LDA >= max(1,N). AF AF is REAL array, dimension (LDAF,N) The triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T, as computed by SPOTRF. LDAF LDAF is INTEGER The leading dimension of the array AF. LDAF >= max(1,N). WORK WORK is REAL array, dimension (2*N) Author Univ. of Tennessee Univ. of California Berkeley Univ. of Colorado Denver NAG Ltd. doubleprecisionfunctionzla_porpvgrw(character*1uplo,integerncols,complex*16,dimension(lda,*)a,integerlda,complex*16,dimension(ldaf,*)af,integerldaf,doubleprecision,dimension(*)work)ZLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. Purpose: ZLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is used. If this is much less than 1, the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, estimated condition numbers, and error bounds could be unreliable. ParametersUPLO UPLO is CHARACTER*1 = 'U': Upper triangle of A is stored; = 'L': Lower triangle of A is stored. NCOLS NCOLS is INTEGER The number of columns of the matrix A. NCOLS >= 0. A A is COMPLEX*16 array, dimension (LDA,N) On entry, the N-by-N matrix A. LDA LDA is INTEGER The leading dimension of the array A. LDA >= max(1,N). AF AF is COMPLEX*16 array, dimension (LDAF,N) The triangular factor U or L from the Cholesky factorization A = U**T*U or A = L*L**T, as computed by ZPOTRF. LDAF LDAF is INTEGER The leading dimension of the array AF. LDAF >= max(1,N). WORK WORK is DOUBLE PRECISION array, dimension (2*N) Author Univ. of Tennessee Univ. of California Berkeley Univ. of Colorado Denver NAG Ltd.

la_porpvgrw - la_porpvgrw: reciprocal pivot growth

Functions real function cla_porpvgrw (uplo, ncols, a, lda, af, ldaf, work) CLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. double precision function dla_porpvgrw (uplo, ncols, a, lda, af, ldaf, work) DLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. real function sla_porpvgrw (uplo, ncols, a, lda, af, ldaf, work) SLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix. double precision function zla_porpvgrw (uplo, ncols, a, lda, af, ldaf, work) ZLA_PORPVGRW computes the reciprocal pivot growth factor norm(A)/norm(U) for a symmetric or Hermitian positive-definite matrix.