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

📦 version 3.12.0, in opensuse-leap 16.0

💖 dgesvxx.f(3) — Library Functions Manual 💖

Okay bestie, this is the manual page for dgesvxx.f, and it is lowkey iconic. Let’s get into it ✨

🌸 NAME 🌸

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

✨ SYNOPSIS ✨

🫶 Functions/Subroutines 🫶

subroutine DGESVXX (fact, trans, n, nrhs, a, lda, af, ldaf, ipiv, equed, r, c, b, ldb, x, ldx, rcond, rpvgrw, berr, n_err_bnds, err_bnds_norm, err_bnds_comp, nparams, params, work, iwork, info)

DGESVXX computes the solution to system of linear equations A * X = B for GE matrices — that’s the whole main-character job, fr fr 💅

🌟 Function/Subroutine Documentation 🌟

👑 subroutine DGESVXX 👑

subroutine DGESVXX (character fact, character trans, integer n, integer nrhs, double precision, dimension( lda, * ) a, integer lda, double precision, dimension( ldaf, * ) af, integer ldaf, integer, dimension( * ) ipiv, character equed, double precision, dimension( * ) r, double precision, dimension( * ) c, double precision, dimension( ldb, * ) b, integer ldb, double precision, dimension( ldx , * ) x, integer ldx, double precision rcond, double precision rpvgrw, 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, double precision, dimension( * ) work, integer, dimension( * ) iwork, integer info)

DGESVXX computes the solution to system of linear equations A * X = B for GE matrices

🎀 Purpose 🎀

Okay so here’s the tea, bestie: DGESVXX uses the LU factorization to compute the solution to a double precision system of linear equations A * X = B, where A is an N-by-N matrix and X and B are N-by-NRHS matrices.

If requested, both normwise and maximum componentwise error bounds are returned. DGESVXX will return a solution with a tiny guaranteed error (O(eps) where eps is the working machine precision) unless the matrix is very ill-conditioned, in which case a warning is returned. Relevant condition numbers also are calculated and returned. ✨ math with receipts ✨

DGESVXX accepts user-provided factorizations and equilibration factors; see the definitions of the FACT and EQUED options. Solving with refinement and using a factorization from a previous DGESVXX call will also produce a solution with either O(eps) errors or warnings, but we cannot make that claim for general user-provided factorizations and equilibration factors if they differ from what DGESVXX would itself produce. No cap, that caveat matters.

💫 Description 💫

The following steps are performed:

  1. If FACT = 'E', double precision scaling factors are computed to equilibrate the system:
      TRANS = 'N':  diag(R)*A*diag(C)     *inv(diag(C))*X = diag(R)*B
      TRANS = 'T': (diag(R)*A*diag(C))**T *inv(diag(R))*X = diag(C)*B
      TRANS = 'C': (diag(R)*A*diag(C))**H *inv(diag(R))*X = diag(C)*B

Whether or not the system will be equilibrated depends on the scaling of the matrix A, but if equilibration is used, A is overwritten by diag(R)*A*diag(C) and B by diag(R)*B (if TRANS='N') or diag(C)*B (if TRANS = 'T' or 'C').

  1. If FACT = 'N' or 'E', the LU decomposition is used to factor the matrix A (after equilibration if FACT = 'E') as
A = P * L * U,

where P is a permutation matrix, L is a unit lower triangular matrix, and U is upper triangular. Permutation, lower, upper — the holy trinity of LU, iykyk.

  1. If some U(i,i)=0, so that U is exactly singular, then the routine returns with INFO = i. Otherwise, the factored form of A is used to estimate the condition number of the matrix A (see argument RCOND). If the reciprocal of the condition number is less than machine precision, the routine still goes on to solve for X and compute error bounds as described below.
  2. The system of equations is solved for X using the factored form of A. Slay.
  3. By default (unless PARAMS(LA_LINRX_ITREF_I) is set to zero), the routine will use iterative refinement to try to get a small error and error bounds. Refinement calculates the residual to at least twice the working precision.
  4. If equilibration was used, the matrix X is premultiplied by diag(C) (if TRANS = 'N') or diag(R) (if TRANS = 'T' or 'C') so that it solves the original system before equilibration.

Some optional parameters are bundled in the PARAMS array. These settings determine how refinement is performed, but often the defaults are acceptable. If the defaults are acceptable, users can pass NPARAMS = 0 which prevents the source code from accessing the PARAMS argument. Defaults are lowkey the moment.

🍓 Parameters 🍓

💅 FACT

FACT is CHARACTER*1. It specifies whether or not the factored form of the matrix A is supplied on entry, and if not, whether the matrix A should be equilibrated before it is factored.

  • = 'F': On entry, AF and IPIV contain the factored form of A. If EQUED is not 'N', the matrix A has been equilibrated with scaling factors given by R and C. A, AF, and IPIV are not modified.
  • = 'N': The matrix A will be copied to AF and factored.
  • = 'E': The matrix A will be equilibrated if necessary, then copied to AF and factored.

🌈 TRANS

TRANS is CHARACTER*1. It specifies the form of the system of equations:

  • = 'N': A * X = B (No transpose)
  • = 'T': A**T * X = B (Transpose)
  • = 'C': A**H * X = B (Conjugate Transpose = Transpose)

🦋 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 matrices B and X. NRHS >= 0.

💜 A

A is DOUBLE PRECISION array, dimension (LDA,N). On entry, the N-by-N matrix A. If FACT = 'F' and EQUED is not 'N', then A must have been equilibrated by the scaling factors in R and/or C. A is not modified if FACT = 'F' or 'N', or if FACT = 'E' and EQUED = 'N' on exit.

On exit, if EQUED .ne. 'N', A is scaled as follows:

  • EQUED = 'R': A := diag(R) * A
  • EQUED = 'C': A := A * diag(C)
  • EQUED = 'B': A := diag(R) * A * diag(C).

🧚 LDA

LDA is INTEGER. The leading dimension of the array A. LDA >= max(1,N).

🍓 AF

AF is DOUBLE PRECISION array, dimension (LDAF,N). If FACT = 'F', then AF is an input argument and on entry contains the factors L and U from the factorization A = P*L*U as computed by DGETRF. If EQUED .ne. 'N', then AF is the factored form of the equilibrated matrix A.

If FACT = 'N', then AF is an output argument and on exit returns the factors L and U from the factorization A = P*L*U of the original matrix A.

If FACT = 'E', then AF is an output argument and on exit returns the factors L and U from the factorization A = P*L*U of the equilibrated matrix A (see the description of A for the form of the equilibrated matrix).

🌸 LDAF

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

👑 IPIV

IPIV is INTEGER array, dimension (N). If FACT = 'F', then IPIV is an input argument and on entry contains the pivot indices from the factorization A = P*L*U as computed by DGETRF; row i of the matrix was interchanged with row IPIV(i).

If FACT = 'N', then IPIV is an output argument and on exit contains the pivot indices from the factorization A = P*L*U of the original matrix A.

If FACT = 'E', then IPIV is an output argument and on exit contains the pivot indices from the factorization A = P*L*U of the equilibrated matrix A.

🫶 EQUED

EQUED is CHARACTER*1. It specifies the form of equilibration that was done.

  • = 'N': No equilibration (always true if FACT = 'N').
  • = 'R': Row equilibration, i.e., A has been premultiplied by diag(R).
  • = 'C': Column equilibration, i.e., A has been postmultiplied by diag(C).
  • = 'B': Both row and column equilibration, i.e., A has been replaced by diag(R) * A * diag(C).

EQUED is an input argument if FACT = 'F'; otherwise, it is an output argument.

🔥 R

R is DOUBLE PRECISION array, dimension (N). The row scale factors for A. If EQUED = 'R' or 'B', A is multiplied on the left by diag(R); if EQUED = 'N' or 'C', R is not accessed. R is an input argument if FACT = 'F'; otherwise, R is an output argument. If FACT = 'F' and EQUED = 'R' or 'B', each element of R must be positive.

If R is output, each element of R is a power of the radix.

Heads up, bestie: If R is input, each element of R 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.

🍓 C

C — (C’s full description is coming in part 2, bestie 🫶)

💖 C 💖

C is DOUBLE PRECISION array, dimension (N) — the column scale factors for A, bestie.

  • If EQUED = 'C' or 'B', then A gets multiplied on the right by diag(C) 🫶
  • If EQUED = 'N' or 'R', then C is literally not even accessed, she's off duty 💅

C is an input argument if FACT = 'F'; otherwise it's an output argument. And if FACT = 'F' and EQUED = 'C' or 'B', every single element of C must be positive, no exceptions ✨

When C is output, each element of C is a power of the radix. When C is input, each element should be a power of the radix so you get a reliable solution and reliable error estimates. Scaling by powers of the radix doesn't 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, and that produces error estimates that may not be reliable. So like… be careful, fr fr.

🌈 B 🌈

B is DOUBLE PRECISION array, dimension (LDB,NRHS).

On entry: the N-by-NRHS right hand side matrix B. Iconic. On exit, it depends:

  • if EQUED = 'N', B is not modified
  • if TRANS = 'N' and EQUED = 'R' or 'B', B is overwritten by diag(R)*B
  • if TRANS = 'T' or 'C' and EQUED = 'C' or 'B', B is overwritten by diag(C)*B

🎀 LDB 🎀

LDB is INTEGER — the leading dimension of the array B. And LDB >= max(1,N), periodt.

🦋 X 🦋

X is DOUBLE PRECISION array, dimension (LDX,NRHS).

If INFO = 0, this is the N-by-NRHS solution matrix X to the original system of equations. Lowkey important note: A and B are modified on exit if EQUED .ne. 'N', and the solution to the equilibrated system is:

inv(diag(C))*X if TRANS = 'N' and EQUED = 'C' or 'B', or
inv(diag(R))*X if TRANS = 'T' or 'C' and EQUED = 'R' or 'B'.

So yeah, the X you get is for the equilibrated system, not the original one — iykyk 🌸

⭐ LDX ⭐

LDX is INTEGER — the leading dimension of the array X. LDX >= max(1,N). Same energy as LDB 💅

💀 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 that happened). If this is less than the machine precision (in particular, if it is zero), the matrix is singular to working precision. Big yikes.

Note that the error may still be small even if this number is very small and the matrix appears ill-conditioned. So don't panic immediately, but like… keep an eye on it 👀

🔥 RPVGRW 🔥

RPVGRW is DOUBLE PRECISION — reciprocal pivot growth.

On exit, this contains the reciprocal pivot growth factor norm(A)/norm(U). The norm is used. If this is much less than 1, then the stability of the LU factorization of the (equilibrated) matrix A could be poor 😬 Which also means the solution X, the estimated condition numbers, and the error bounds could be unreliable.

If factorization fails with 0<INFO<=N, then this contains the reciprocal pivot growth factor for the leading INFO columns of A. In DGESVX, this quantity is returned in WORK(1). 💖

🍓 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. She ate, honestly 👑

🧚 N_ERR_BNDS 🧚

N_ERR_BNDS is INTEGER — the 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 info about various error bounds and condition numbers corresponding to the normwise relative error, which is defined like this:

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) * 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 . 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 💜

ERR_BNDS_COMP is DOUBLE PRECISION array, dimension (NRHS, N_ERR_BNDS).

For each right-hand side, this array contains info about various error bounds and condition numbers corresponding to the componentwise relative error, defined as:

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 (componentwise relative error depends on her, main character energy), 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. And 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) * 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 componentwise reciprocal condition number. Compared with the threshold sqrt(n) * dlamch('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.

See Lapack Working Note 165 for further details and extra cautions 📓 (yes, again — she's referenced by both, that's her brand 💅)

✨ NPARAMS ✨

NPARAMS is INTEGER — specifies the number of parameters set in PARAMS.

If <= 0, the PARAMS array is never referenced and default values are used. Clean, effortless, defaults-only vibes 🧚

👑 PARAMS 👑

PARAMS is DOUBLE PRECISION array, dimension (NPARAMS) — specifies algorithm parameters.

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 🫶

  • 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 extra-precise refinement algorithm.
    • (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. 💀
  • 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 DOUBLE PRECISION array, dimension (4*N). That's it, that's the whole vibe. Four times N, slay 🎀

💅 IWORK 💅

IWORK

IWORK — an INTEGER array, dimension (N). That's the whole note for this one; she's lowkey chill. 🎀

INFO — an INTEGER, and this is the main character of the error-reporting section. Here's the full tea, no skipped values:

  • = 0: successful exit. The solution to every right-hand side is guaranteed. slay, we love a clean win.
  • < 0: if INFO = -i, the i-th argument had an illegal value. not cute, check your args.
  • > 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. big yikes — singular energy.
  • = 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. iykyk, the error-bound girls are typing.
This is the full INFO dictionary, bestie — don't skip the = N+J paragraph, it's long but it's doing important work. 💅

✨ Author ✨

The credits, because we respect the icons:

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

Definition at line 535 of file dgesvxx.f.

✨ Author (auto-generated edition) ✨

Generated automatically by Doxygen for LAPACK from the source code.

💖 And that's a wrap on the dgesvxx.f(3) saga — LAPACK Version 3.12.0, source /home/abui.../dgesvxx.f(3). The footer didn't give us a date, so no invented dates here, bestie; just the version and source. Now go forth and solve those systems. 🫶

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