SLA_SYRFSX_EXTENDED(3)

📦

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🌸 NAME 🌸

SRC/sla_syrfsx_extended.f — Library Functions Manual (3), bestie. That's the file this whole page is about, and yes the title is doing the absolute most. 💅

✨ SYNOPSIS ✨

🎀 Functions/Subroutines 🎀

sla_syrfsx_extended — SLA_SYRFSX_EXTENDED improves the computed solution to a system of linear equations for symmetric indefinite matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution.

Here's the synopsis signature, verbatim so you can copy it and not break anything:

subroutine sla_syrfsx_extended (prec_type, uplo, n, nrhs, a, lda, af,
    ldaf, ipiv, colequ, c, b, ldb, y, ldy, berr_out, n_norms,
    err_bnds_norm, err_bnds_comp, res, ayb, dy, y_tail, rcond, ithresh,
    rthresh, dz_ub, ignore_cwise, info)

🦋 Function/Subroutine Documentation 🦋

Okay so here's the full typed interface, and it's giving main character energy with all these arguments:

subroutine sla_syrfsx_extended (integer prec_type, character uplo, integer
    n, integer nrhs, real, dimension( lda, * ) a, integer lda, real,
    dimension( ldaf, * ) af, integer ldaf, integer, dimension( * ) ipiv,
    logical colequ, real, dimension( * ) c, real, dimension( ldb, * ) b,
    integer ldb, real, dimension( ldy, * ) y, integer ldy, real, dimension(
    * ) berr_out, integer n_norms, real, dimension( nrhs, * ) err_bnds_norm,
    real, dimension( nrhs, * ) err_bnds_comp, real, dimension( * ) res,
    real, dimension( * ) ayb, real, dimension( * ) dy, real, dimension( * )
    y_tail, real rcond, integer ithresh, real rthresh, real dz_ub, logical
    ignore_cwise, integer info)

SLA_SYRFSX_EXTENDED improves the computed solution to a system of linear equations for symmetric indefinite matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution. Iconic, honestly. 🔥

🧚 Purpose 🧚

SLA_SYRFSX_EXTENDED improves the computed solution to a system of linear equations by doing extra-precise iterative refinement, and it provides error bounds plus backward error estimates for the solution. This subroutine is called by SSYRFSX to perform iterative refinement. (iykyk 💖)

Besides the normwise error bound, the code provides the maximum componentwise error bound if possible. See the comments for ERR_BNDS_NORM and ERR_BNDS_COMP for details of the error bounds. Note that this subroutine is only responsible for setting the second fields of ERR_BNDS_NORM and ERR_BNDS_COMP. Periodt. 💅

🧾 Parameters 🧾

PREC_TYPE

PREC_TYPE is INTEGER.

It specifies the intermediate precision to be used in refinement. The value is defined by ILAPREC(P) where P is a CHARACTER and:

P = 'S': Single
P = 'D': Double
P = 'I': Indigenous
P = 'X' or 'E': Extra

UPLO

UPLO is CHARACTER*1.

= 'U': Upper triangle of A is stored;
= 'L': Lower triangle of A is stored.

N

N is INTEGER.

The number of linear equations, i.e., the order of the matrix A. N >= 0.

NRHS

NRHS is INTEGER.

The number of right-hand-sides, i.e., the number of columns of the matrix B.

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 block diagonal matrix D and the multipliers used to obtain the factor U or L as computed by SSYTRF.

LDAF

LDAF is INTEGER.

The leading dimension of the array AF. LDAF >= max(1,N).

IPIV

IPIV is INTEGER array, dimension (N).

Details of the interchanges and the block structure of D as determined by SSYTRF.

COLEQU

COLEQU is LOGICAL.

If .TRUE. then column equilibration was done to A before calling this routine. This is needed to compute the solution and error bounds correctly.

C

C is REAL array, dimension (N).

The column scale factors for A. If COLEQU = .FALSE., C is not accessed. If C is input, each element of C should be a power of the radix to ensure a reliable solution and error estimates. Scaling by powers of the radix does not cause rounding errors unless the result underflows or overflows. Rounding errors during scaling lead to refining with a matrix that is not equivalent to the input matrix, producing error estimates that may not be reliable.

⚠️ Bestie, that last part is lowkey serious: if the scaling math underflows or overflows, the error estimates may not be reliable. No cap. 💀

B

B is REAL array, dimension (LDB,NRHS).

The right-hand-side matrix B.

LDB

LDB is INTEGER.

The leading dimension of the array B. LDB >= max(1,N).

Y

Y is REAL array, dimension (LDY,NRHS).

On entry, the solution matrix X, as computed by SSYTRS. On exit, the improved solution matrix Y.

LDY

LDY is INTEGER.

The leading dimension of the array Y. LDY >= max(1,N).

BERR_OUT

BERR_OUT is REAL array, dimension (NRHS).

On exit, BERR_OUT(j) contains the componentwise relative backward error for right-hand-side j from the formula:

max(i) ( abs(RES(i)) / ( abs(op(A_s))*abs(Y) + abs(B_s) )(i) )

where abs(Z) is the componentwise absolute value of the matrix or vector Z. This is computed by SLA_LIN_BERR.

N_NORMS

N_NORMS is INTEGER.

Determines which error bounds to return (see ERR_BNDS_NORM and ERR_BNDS_COMP).

If N_NORMS >= 1 return normwise error bounds.
If N_NORMS >= 2 return componentwise error bounds.

ERR_BNDS_NORM

ERR_BNDS_NORM is REAL array, dimension (NRHS, N_ERR_BNDS).

For each right-hand side, this array contains information about various error bounds and condition numbers corresponding to the normwise relative error, which is defined as follows:

Normwise relative error in the ith solution vector:

            max_j (abs(XTRUE(j,i) - X(j,i)))
           ------------------------------
                 max_j abs(X(j,i))

The array is indexed by the type of error information as described below. There currently are up to three pieces of information returned.

The first index in ERR_BNDS_NORM(i,:) corresponds to the ith right-hand side.

The second index in ERR_BNDS_NORM(:,err) contains the following three fields:

err = 1 — boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * slamch('Epsilon').
err = 2 — error bound: The estimated forward error, almost certainly within a factor of 10 of the true error so long as the next entry is greater than the threshold sqrt(n) * slamch('Epsilon'). This error bound should only be trusted if the previous boolean is true.
err = 3 — Reciprocal condition number: Estimated normwise reciprocal condition number. Compared with the threshold sqrt(n) * slamch('Epsilon') to determine if the error estimate is . These reciprocal condition numbers are 1 / (norm(Z^{-1},inf) * norm(Z,inf)) for some appropriately scaled matrix Z. Let Z = S*A, where S scales each row by a power of the radix so all absolute row sums of Z are approximately 1.

This subroutine is only responsible for setting the second field above. See Lapack Working Note 165 for further details and extra cautions.

ERR_BNDS_COMP

ERR_BNDS_COMP — REAL array, dimension (NRHS, N_ERR_BNDS). Okay bestie, for each right-hand side this array is carrying the error-bound and condition-number tea for the componentwise relative error. The componentwise relative error in the ith solution vector is literally:

                    abs(XTRUE(j,i) - X(j,i))
             max_j ----------------------
                         abs(X(j,i))

The array is indexed by the right-hand side i (on which the componentwise relative error depends), and the type of error information as described below. There currently are up to three pieces of information returned for each right-hand side. If componentwise accuracy is not requested (PARAMS(3) = 0.0), then ERR_BNDS_COMP is not accessed. If N_ERR_BNDS < 3, then at most the first (:,N_ERR_BNDS) entries are returned.

The first index in ERR_BNDS_COMP(i,:) corresponds to the ith right-hand side.

The second index in ERR_BNDS_COMP(:,err) contains the following three fields:

err = 1 — boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * slamch('Epsilon').
err = 2 — error bound: The estimated forward error, almost certainly within a factor of 10 of the true error so long as the next entry is greater than the threshold sqrt(n) * slamch('Epsilon'). This error bound should only be trusted if the previous boolean is true.
err = 3 — Reciprocal condition number: Estimated componentwise reciprocal condition number. Compared with the threshold sqrt(n) * slamch('Epsilon') to determine if the error estimate is . These reciprocal condition numbers are 1 / (norm(Z^{-1},inf) * norm(Z,inf)) for some appropriately scaled matrix Z. Let Z = S*(A*diag(x)), where x is the solution for the current right-hand side and S scales each row of A*diag(x) by a power of the radix so all absolute row sums of Z are approximately 1.

This subroutine is only responsible for setting the second field above. See Lapack Working Note 165 for further details and extra cautions.

RES — REAL array, dimension (N). Workspace to hold the intermediate residual.
AYB — REAL array, dimension (N). Workspace. This can be the same workspace passed for Y_TAIL.
DY — REAL array, dimension (N). Workspace to hold the intermediate solution.
Y_TAIL — REAL array, dimension (N). Workspace to hold the trailing bits of the intermediate solution.
RCOND — REAL. Reciprocal scaled condition number. This is an estimate of the reciprocal Skeel condition number of the matrix A after equilibration (if done). If this is less than the machine precision (in particular, if it is zero), the matrix is singular to working precision. Note that the error may still be small even if this number is very small and the matrix appears ill-conditioned.
ITHRESH — INTEGER. The maximum number of residual computations allowed for refinement. The default is 10. For 'aggressive' set to 100 to permit convergence using approximate factorizations or factorizations other than LU. If the factorization uses a technique other than Gaussian elimination, the guarantees in ERR_BNDS_NORM and ERR_BNDS_COMP may no longer be trustworthy.
RTHRESH — REAL. Determines when to stop refinement if the error estimate stops decreasing. Refinement will stop when the next solution no longer satisfies norm(dx_{i+1}) < RTHRESH * norm(dx_i) where norm(Z) is the infinity norm of Z. RTHRESH satisfies 0 < RTHRESH <= 1. The default value is 0.5. For 'aggressive' set to 0.9 to permit convergence on extremely ill-conditioned matrices. See LAWN 165 for more details.
DZ_UB — REAL. Determines when to start considering componentwise convergence. Componentwise convergence is only considered after each component of the solution Y is stable, which we define as the relative change in each component being less than DZ_UB. The default value is 0.25, requiring the first bit to be stable. See LAWN 165 for more details.
IGNORE_CWISE — LOGICAL. If .TRUE. then ignore componentwise convergence. Default value is .FALSE..
INFO — INTEGER
- = 0: Successful exit.
- < 0: if INFO = -i, the ith argument to SLA_SYRFSX_EXTENDED had an illegal value

💖 Author

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

Definition at line 389 of file sla_syrfsx_extended.f.

💖 Author (Doxygen's autogenerated note)

Generated automatically by Doxygen for LAPACK from the source code.

Signing off, bestie: this page is LAPACK Version 3.12.0, source SRC/sla_sy..._extended.f(3) — and the date is whatever your footer timestamp says, because this snippet didn't hand me one, no invented tea, periodt 🎀💅

📦 where this page comes from

arch rolling: lapack-doc 3.12.1-2 · custom
💖 girlified by deepseek-ai/DeepSeek-V4.1-Flash-1017 on 2026-11-04: a language model's rewrite of the original, under its terms