|
| pure real(sp) function, public | la_lapack_others_sm::la_sla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | SLA_GERPVGRW: 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.
|
| |
| pure real(dp) function, public | la_lapack_others_sm::la_dla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | DLA_GERPVGRW: 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.
|
| |
| pure real(qp) function, public | la_lapack_others_sm::la_qla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | QLA_GERPVGRW: 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.
|
| |
| subroutine, public | la_lapack_others_sm::la_sla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | SLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| subroutine, public | la_lapack_others_sm::la_dla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | DLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| subroutine, public | la_lapack_others_sm::la_qla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | QLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_sla_syrcond (uplo, n, a, lda, af, ldaf, ipiv, cmode, c, info, work, iwork) |
| | SLA_SYRCOND: estimates the Skeel condition number of op(A) * op2(C) where op2 is determined by CMODE as follows CMODE = 1 op2(C) = C CMODE = 0 op2(C) = I CMODE = -1 op2(C) = inv(C) The Skeel condition number cond(A) = norminf( |inv(A)||A| ) is computed by computing scaling factors R such that diag(R)*A*op2(C) is row equilibrated and computing the standard infinity-norm condition number.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_dla_syrcond (uplo, n, a, lda, af, ldaf, ipiv, cmode, c, info, work, iwork) |
| | DLA_SYRCOND: estimates the Skeel condition number of op(A) * op2(C) where op2 is determined by CMODE as follows CMODE = 1 op2(C) = C CMODE = 0 op2(C) = I CMODE = -1 op2(C) = inv(C) The Skeel condition number cond(A) = norminf( |inv(A)||A| ) is computed by computing scaling factors R such that diag(R)*A*op2(C) is row equilibrated and computing the standard infinity-norm condition number.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_qla_syrcond (uplo, n, a, lda, af, ldaf, ipiv, cmode, c, info, work, iwork) |
| | QLA_SYRCOND: estimates the Skeel condition number of op(A) * op2(C) where op2 is determined by CMODE as follows CMODE = 1 op2(C) = C CMODE = 0 op2(C) = I CMODE = -1 op2(C) = inv(C) The Skeel condition number cond(A) = norminf( |inv(A)||A| ) is computed by computing scaling factors R such that diag(R)*A*op2(C) is row equilibrated and computing the standard infinity-norm condition number.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_sla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | SLA_SYRPVGRW: 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.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_dla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | DLA_SYRPVGRW: 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.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_qla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | QLA_SYRPVGRW: 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.
|
| |
| pure real(sp) function, public | la_lapack_others_sm::la_cla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | CLA_GERPVGRW: 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.
|
| |
| pure real(dp) function, public | la_lapack_others_sm::la_zla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | ZLA_GERPVGRW: 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.
|
| |
| pure real(qp) function, public | la_lapack_others_sm::la_wla_gerpvgrw (n, ncols, a, lda, af, ldaf) |
| | WLA_GERPVGRW: 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.
|
| |
| subroutine, public | la_lapack_others_sm::la_cla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | CLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| subroutine, public | la_lapack_others_sm::la_zla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | ZLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| subroutine, public | la_lapack_others_sm::la_wla_syamv (uplo, n, alpha, a, lda, x, incx, beta, y, incy) |
| | WLA_SYAMV: performs the matrix-vector operation y := alpha*abs(A)*abs(x) + beta*abs(y), where alpha and beta are scalars, x and y are vectors and A is an n by n symmetric matrix. This function is primarily used in calculating error bounds. To protect against underflow during evaluation, components in the resulting vector are perturbed away from zero by (N+1) times the underflow threshold. To prevent unnecessarily large errors for block-structure embedded in general matrices, "symbolically" zero components are not perturbed. A zero entry is considered "symbolic" if all multiplications involved in computing that entry have at least one zero multiplicand.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_gbrcond_c (trans, n, kl, ku, ab, ldab, afb, ldafb, ipiv, c, capply, info, work, rwork) |
| | CLA_GBRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a REAL vector.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_gbrcond_c (trans, n, kl, ku, ab, ldab, afb, ldafb, ipiv, c, capply, info, work, rwork) |
| | ZLA_GBRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a DOUBLE PRECISION vector.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_gbrcond_c (trans, n, kl, ku, ab, ldab, afb, ldafb, ipiv, c, capply, info, work, rwork) |
| | WLA_GBRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a QUAD PRECISION vector.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_gercond_c (trans, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | CLA_GERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a REAL vector.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_gercond_c (trans, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | ZLA_GERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a DOUBLE PRECISION vector.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_gercond_c (trans, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | WLA_GERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a QUAD PRECISION vector.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_hercond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | CLA_HERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a REAL vector.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_hercond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | ZLA_HERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a DOUBLE PRECISION vector.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_hercond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | WLA_HERCOND_C: computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a QUAD PRECISION vector.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_porcond_c (uplo, n, a, lda, af, ldaf, c, capply, info, work, rwork) |
| | CLA_PORCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a REAL vector.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_porcond_c (uplo, n, a, lda, af, ldaf, c, capply, info, work, rwork) |
| | ZLA_PORCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a DOUBLE PRECISION vector.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_porcond_c (uplo, n, a, lda, af, ldaf, c, capply, info, work, rwork) |
| | WLA_PORCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a QUAD PRECISION vector.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_syrcond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | CLA_SYRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a REAL vector.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_syrcond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | ZLA_SYRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a DOUBLE PRECISION vector.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_syrcond_c (uplo, n, a, lda, af, ldaf, ipiv, c, capply, info, work, rwork) |
| | WLA_SYRCOND_C: Computes the infinity norm condition number of op(A) * inv(diag(C)) where C is a QUAD PRECISION vector.
|
| |
| real(sp) function, public | la_lapack_others_sm::la_cla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | CLA_SYRPVGRW: 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.
|
| |
| real(dp) function, public | la_lapack_others_sm::la_zla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | ZLA_SYRPVGRW: 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.
|
| |
| real(qp) function, public | la_lapack_others_sm::la_wla_syrpvgrw (uplo, n, info, a, lda, af, ldaf, ipiv, work) |
| | WLA_SYRPVGRW: 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.
|
| |
1.13.2