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

/home/abui...extended.f(3) — Library Functions Manual 💅

💖 NAME 💖

Okay bestie, this whole page is literally about this file:

/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/dla_porfsx_extended.f

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✨ SYNOPSIS ✨

🌸 Functions/Subroutines 🌸

subroutine DLA_PORFSX_EXTENDED (prec_type, uplo, n, nrhs, a, lda, af, ldaf, 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)

DLA_PORFSX_EXTENDED improves the computed solution to a system of linear equations for symmetric or Hermitian positive-definite matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution. It’s giving “I fixed your answer and also brought receipts” energy 🔥

🦋 Function/Subroutine Documentation 🦋

Here’s the full typed-out signature again, because she is extra and we respect it:

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

DLA_PORFSX_EXTENDED improves the computed solution to a system of linear equations for symmetric or Hermitian positive-definite matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution. No cap, she’s doing the absolute most for your linear system 💖

🎀 Purpose 🎀

So DLA_PORFSX_EXTENDED improves the computed solution to a system of linear equations by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution. This subroutine is called by DPORFSX to perform iterative refinement. In addition to normwise error bound, the code provides maximum componentwise error bound if possible. See 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.

Translation: she’s the refinement girlie, and she’s setting exactly the fields she says she sets — no extra credit, no invented behavior ✨

👑 Parameters 👑

Bestie, every single parameter is getting her moment. None are skipped. Periodt.

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 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).

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 DOUBLE PRECISION 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.

Okay this part is serious, bestie: 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.

B

B is DOUBLE PRECISION 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 DOUBLE PRECISION array, dimension (LDY,NRHS). On entry, the solution matrix X, as computed by DPOTRS. 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 DOUBLE PRECISION 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 DLA_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 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 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

okay so boom — ERR_BNDS_COMP is a DOUBLE PRECISION array, dimension (NRHS, N_ERR_BNDS). For each right-hand side, this array is literally carrying info about various error bounds and condition numbers for the componentwise relative error, defined like this:

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

The array is indexed by the right-hand side i (the one the componentwise relative error depends on) and by the type of error info, bestie. There currently are up to three pieces of info 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

RES is DOUBLE PRECISION array, dimension (N). Workspace to hold the intermediate residual. simple, iconic, doing its job 💅

🫧 AYB

AYB is DOUBLE PRECISION array, dimension (N). Workspace. This can be the same workspace passed for Y_TAIL — reuse era, bestie.

🧚 DY

DY is DOUBLE PRECISION array, dimension (N). Workspace to hold the intermediate solution.

🌸 Y_TAIL

Y_TAIL is DOUBLE PRECISION array, dimension (N). Workspace to hold the trailing bits of the intermediate solution.

💜 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. Note that the error may still be small even if this number is very small and the matrix appears ill-conditioned.

🔥 ITHRESH

ITHRESH is 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

RTHRESH is DOUBLE PRECISION. 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

DZ_UB is DOUBLE PRECISION. 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

IGNORE_CWISE is LOGICAL. If .TRUE. then ignore componentwise convergence. Default value is .FALSE..

⭐ INFO

INFO is INTEGER.

• = 0: Successful exit.
• < 0: if INFO = -i, the ith argument to DPOTRS had an illegal value. yes, DPOTRS — that’s what it says, bestie, we don’t edit the lore 💀

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🌟 Author

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

Definition at line 380 of file dla_porfsx_extended.f.

🌸 Author

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xoxo, bestie 💖 — LAPACK Version 3.12.0, source /home/abui...extended.f(3); no date is showing in this footer crop, and that’s the whole sign-off vibe 🫶