/home/abui.../zsyrfsx.f(3)

📦 version 3.12.0, in opensuse-leap 16.0

💅 NAME

This page is all about ZSYRFSX, bestie — the LAPACK routine whose source lives at /home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/zsyrfsx.f. It's page zsyrfsx.f(3) out of the Library Functions Manual, and it is a lot, so buckle up 🎀

✨ SYNOPSIS ✨

🦋 Functions/Subroutines

subroutine ZSYRFSX (uplo, equed, n, nrhs, a, lda, af, ldaf, ipiv, s, b,
    ldb, x, ldx, rcond, berr, n_err_bnds, err_bnds_norm, err_bnds_comp,
    nparams, params, work, rwork, info)

ZSYRFSX — that's the whole cast of characters, no cap 💅

🌈 Function/Subroutine Documentation 🌈

subroutine ZSYRFSX (character uplo, character equed, integer n, integer
    nrhs, complex*16, dimension( lda, * ) a, integer lda, complex*16,
    dimension( ldaf, * ) af, integer ldaf, integer, dimension( * ) ipiv,
    double precision, dimension( * ) s, complex*16, dimension( ldb, * ) b,
    integer ldb, complex*16, dimension( ldx, * ) x, integer ldx, double
    precision rcond, double precision, dimension( * ) berr, integer
    n_err_bnds, double precision, dimension( nrhs, * ) err_bnds_norm, double
    precision, dimension( nrhs, * ) err_bnds_comp, integer nparams, double
    precision, dimension( * ) params, complex*16, dimension( * ) work,
    double precision, dimension( * ) rwork, integer info)

ZSYRFSX 🫶 (yes, the same name again — that's just how these pages roll)

💖 Purpose:

okay so here's the vibe: ZSYRFSX improves the computed solution to a system of linear equations when the coefficient matrix is symmetric indefinite, and it also provides error bounds and backward error estimates for that solution. In addition to the normwise error bound, the code provides a maximum componentwise error bound if possible. Peep the comments for ERR_BNDS_NORM and ERR_BNDS_COMP for all the deets on those error bounds 👑

The original system of linear equations may have been equilibrated before calling this routine, as described by the arguments EQUED and S below. In that case, the solution and error bounds that come back to you are for the original unequilibrated system — the math is giving "I fixed it but I'm showing you the before pics" 😭

Some optional parameters are bundled in the PARAMS array. These settings determine how refinement is performed, but ngl the defaults are usually acceptable. If the defaults are acceptable to you, users can pass NPARAMS = 0, which prevents the source code from accessing the PARAMS argument. Slay, less work 🎀

🧚 Parameters

UPLO

UPLO is CHARACTER*1 ✨

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

EQUED

EQUED is CHARACTER*1. It specifies the form of equilibration that was done to A before calling this routine. This is needed to compute the solution and error bounds correctly 🫶

  • = 'N': No equilibration
  • = 'Y': Both row and column equilibration, i.e., A has been replaced by diag(S) * A * diag(S). The right hand side B has been changed accordingly.

N

N is INTEGER. It's 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 matrices B and X. NRHS >= 0 💜

A

A is COMPLEX*16 array, dimension (LDA,N). The symmetric matrix A. If UPLO = 'U', the leading N-by-N upper triangular part of A contains the upper triangular part of the matrix A, and the strictly lower triangular part of A is not referenced. If UPLO = 'L', the leading N-by-N lower triangular part of A contains the lower triangular part of the matrix A, and the strictly upper triangular part of A is not referenced. It's giving "half the matrix is literally not invited to the party" 🙈

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 factored form of the matrix A. AF contains the block diagonal matrix D and the multipliers used to obtain the factor U or L from the factorization A = U*D*U**T or A = L*D*L**T as computed by ZSYTRF. Main character energy, honestly 🌸

LDAF

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

IPIV

IPIV is INTEGER array, dimension (N). All the details of the interchanges and the block structure of D as determined by ZSYTRF 🔥

S

S is DOUBLE PRECISION array, dimension (N). The scale factors for A. If EQUED = 'Y', A is multiplied on the left and right by diag(S).

S is an input argument if FACT = 'F'; otherwise, S is an output argument. If FACT = 'F' and EQUED = 'Y', each element of S must be positive.

If S is output, each element of S is a power of the radix. If S is input, each element of S 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. Not me warning you twice, but also yes 💀

(tiny aside, bestie: the doc mentions FACT here even though FACT isn't one of this routine's arguments — we're just reporting what it says, don't come for me 🙈)

B

B is COMPLEX*16 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) ✨

X

X is COMPLEX*16 array, dimension (LDX,NRHS).

On entry, the solution matrix X, as computed by ZGETRS. On exit, the improved solution matrix X. Glow-up complete 💅

LDX

LDX is INTEGER. The leading dimension of the array X. LDX >= max(1,N) 🎀

RCOND

RCOND is DOUBLE PRECISION. 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.

💀 Heads up: the error may still be small even if this number is very small and the matrix appears ill-conditioned. So don't panic-delete your code on sight, ok?

BERR

BERR is DOUBLE PRECISION array, dimension (NRHS). Componentwise relative backward error. This is the componentwise relative backward error of each solution vector X(j) (i.e., the smallest relative change in any element of A or B that makes X(j) an exact solution) 🫶

N_ERR_BNDS

N_ERR_BNDS is INTEGER. Number of error bounds to return for each right hand side and each type (normwise or componentwise). See ERR_BNDS_NORM and ERR_BNDS_COMP below 👀

ERR_BNDS_NORM

ERR_BNDS_NORM is DOUBLE PRECISION 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 *i*th 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 *i*th 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) * dlamch('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) * dlamch('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) * dlamch('Epsilon') to determine if the error estimate is (the original doc literally drops a word right here, bestie, we're all just living with it) 😭. 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.

See Lapack Working Note 165 for further details and extra cautions 📓

ERR_BNDS_COMP

…and the ERR_BNDS_COMP section is just getting started — it spills into part 2, so don't go anywhere 🦋

✨ ERR_BNDS_COMP ✨

Okay so <pink>ERR_BNDS_COMP</pink> is a DOUBLE PRECISIONarray with dimension(NRHS, N_ERR_BNDS). For each right-hand side, it's absolutely packed with error bounds and condition numbers` corresponding to the componentwise relative error, which is defined like this:

!>     Componentwise relative error in the ith solution vector:
!>                    abs(XTRUE(j,i) - X(j,i))
!>             max_j ----------------------
!>                         abs(X(j,i))

(Yes, bestie, that's a max over j, and it's giving math realness 🧮✨)

The array is indexed by the right-hand side i (because the componentwise relative error depends on it) and the type of error info. There are currently up to three pieces of info returned for each right-hand side. If componentwise accuracy is not requested (PARAMS(3) = 0.0), then <pink>ERR_BNDS_COMP</pink> is literally not accessed. And if N_ERR_BNDS < 3, then at most the first (:,N_ERR_BNDS)` entries are returned. Slay.

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 — a boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * dlamch('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) * dlamch('Epsilon'). This error bound should only be trusted if the previous boolean is true. 👀
  • err = 3 — Reciprocal condition number: the estimated componentwise reciprocal condition number. Compared with the threshold sqrt(n) * dlamch('Epsilon') to determine if the error estimate is . (No, we don't know what word is missing — the original literally says "is ." and we keep it, iconic typo 💅). 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. Math queen behavior 🧮👑

See Lapack Working Note 165 for further details and extra cautions.

✨ NPARAMS ✨

NPARAMS is an INTEGER. It specifies the number of parameters set in PARAMS. If it's <= 0, the PARAMS array is never referenced and default values are used. So if you're feeling lazy, just set it to 0 and let the defaults do their thing 💁‍♀️.

✨ PARAMS ✨

PARAMS is a DOUBLE PRECISION array, dimension NPARAMS. It specifies algorithm parameters. Here's the vibe: if an entry is < 0.0, then that entry will be filled with the default value used for that parameter. Only positions up to NPARAMS are accessed; defaults are used for higher-numbered parameters. Easy peasy.

Now the individual parameters, bestie:

  • PARAMS(LA_LINRX_ITREF_I = 1) — Whether to perform iterative refinement or not.
    • Default: 1.0D+0
    • = 0.0: No refinement is performed, and no error bounds are computed.
    • = 1.0: Use the double-precision refinement algorithm, possibly with doubled-single computations if the compilation environment does not support DOUBLE PRECISION.
    • (other values are reserved for future use)
  • PARAMS(LA_LINRX_ITHRESH_I = 2) — Maximum number of residual computations allowed for refinement.
    • Default: 10
    • > ⚠️ 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. (So like, it might slay but it also might lie. Be careful.)
  • PARAMS(LA_LINRX_CWISE_I = 3) — Flag determining if the code will attempt to find a solution with small componentwise relative error in the double-precision algorithm. Positive is true, 0.0 is false.
    • Default: 1.0 (attempt componentwise convergence)

✨ WORK ✨

WORK is a COMPLEX*16 array, dimension (2*N). It's the workspace, doing its thing. 🛠️

✨ RWORK ✨

RWORK is a DOUBLE PRECISION array, dimension (2*N). More workspace, but this time it's real-valued (hence the R, iykyk). 🌟

✨ INFO ✨

INFO is an INTEGER, and it's the usual exit-status bestie. Here's what it means:

  • = 0: Successful exit. The solution to every right-hand side is guaranteed. You ate that. 🎉
  • < 0: If INFO = -i, the i-th argument had an illegal value. Somebody passed something messy. 🙈
  • > 0 and <= N: U(INFO,INFO) is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution and error bounds could not be computed. RCOND = 0 is returned. So like, it tried, but the math said no. 😭
  • = N+J: The solution corresponding to the Jth right-hand side is not guaranteed. The solutions corresponding to other right-hand sides K with K > J may not be guaranteed as well, but only the first such right-hand side is reported. If a small componentwise error is not requested (PARAMS(3) = 0.0) then the Jth right-hand side is the first with a normwise error bound that is not guaranteed (the smallest J such that ERR_BNDS_NORM(J,1) = 0.0). By default (PARAMS(3) = 1.0) the Jth right-hand side is the first with either a normwise or componentwise error bound that is not guaranteed (the smallest J such that either ERR_BNDS_NORM(J,1) = 0.0 or ERR_BNDS_COMP(J,1) = 0.0). See the definition of ERR_BNDS_NORM(:,1) and ERR_BNDS_COMP(:,1). To get information about all of the right-hand sides check ERR_BNDS_NORM or ERR_BNDS_COMP. Basically, if you see N+J, something's sus and you need to investigate further. 🔍

✨ Author ✨

This iconic code was brought to you by:

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

Give them their flowers 💐.

Definition at line 398 of file zsyrfsx.f.

✨ Author ✨

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This page is from LAPACK Version 3.12.0 and is for /home/abui.../zsyrfsx.f(3). Stay slay, bestie. 💅✨

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