SRC/zgesvx.f(3)

📦

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SRC/zgesvx.f(3) — Library Functions Manual 🦋

🌸 NAME 🌸

SRC/zgesvx.f — bestie, this is the page for the ZGESVX subroutine, and yes it's giving Library Functions Manual energy. 💅

✨ SYNOPSIS ✨

🎀 Functions/Subroutines 🎀

subroutine zgesvx (fact, trans, n, nrhs, a, lda, af, ldaf, ipiv, equed,
     r, c, b, ldb, x, ldx, rcond, ferr, berr, work, rwork, info)
     ZGESVX computes the solution to system of linear equations A * X =
     B for GE matrices

💅 Function/Subroutine Documentation 💅

🌟 subroutine zgesvx 🌟

subroutine zgesvx (character fact, character trans, integer n, integer nrhs,
     complex*16, dimension( lda, * ) a, integer lda, complex*16, dimension(
     ldaf, * ) af, integer ldaf, integer, dimension( * ) ipiv, character
     equed, double precision, dimension( * ) r, double precision, dimension(
     * ) c, complex*16, dimension( ldb, * ) b, integer ldb, complex*16,
     dimension( ldx, * ) x, integer ldx, double precision rcond, double
     precision, dimension( * ) ferr, double precision, dimension( * ) berr,
     complex*16, dimension( * ) work, double precision, dimension( * ) rwork,
     integer info)

Okay so ZGESVX computes the solution to system of linear equations A * X = B for GE matrices. Slay. 💖

🌸 Purpose 🌸

Bestie, ZGESVX uses the LU factorization to compute the solution to a complex system of linear equations

   A * X = B,

where A is an N-by-N matrix and X and B are N-by-NRHS matrices.

Error bounds on the solution and a condition estimate are also provided. 🫶

🦋 Description 🦋

The following steps are performed — yes, the whole glow-up routine:

If FACT = 'E', real 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'). 🫶

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. Slay.

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. If the reciprocal of the condition number is less than machine precision, INFO = N+1 is returned as a warning, but the routine still goes on to solve for X and compute error bounds as described below. 😭
The system of equations is solved for X using the factored form of A.
Iterative refinement is applied to improve the computed solution matrix and calculate error bounds and backward error estimates for it.
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. 💅

🍓 Parameters 🍓

FACT — FACT is CHARACTER*1. 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. Values:
- '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. 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)
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 COMPLEX*16 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 COMPLEX*16 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 ZGETRF. 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 ZGETRF; 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. Specifies the form of equilibration that was done. EQUED is an input argument if FACT = 'F'; otherwise, it is an output argument. Values:
- '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).
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.
C — C is DOUBLE PRECISION array, dimension (N). The column scale factors for A. If EQUED = 'C' or 'B', A is multiplied on the right by diag(C); if EQUED = 'N' or 'R', C is not accessed. C is an input argument if FACT = 'F'; otherwise, C is an output argument. If FACT = 'F' and EQUED = 'C' or 'B', each element of C must be positive.
B — B is COMPLEX*16 array, dimension (LDB,NRHS). On entry, the N-by-NRHS right hand side matrix B. On exit:
- 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. LDB >= max(1,N).
X — X is COMPLEX*16 array, dimension (LDX,NRHS). If INFO = 0 or INFO = N+1, the N-by-NRHS solution matrix X to the original system of equations. Note that 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'.
LDX — LDX is INTEGER. The leading dimension of the array X. LDX >= max(1,N).
RCOND — RCOND is DOUBLE PRECISION. The estimate of the reciprocal condition number of the matrix A after equilibration (if done). If RCOND is less than the machine precision (in particular, if RCOND = 0), the matrix is singular to working precision. This condition is indicated by a return code of INFO > 0.

🌈 FERR 🌈

🫶 `FERR`

FERR is a DOUBLE PRECISION array, dimension (NRHS).

Okay so bestie, this is the estimated forward error bound for each solution vector X(j) — that's the j-th column of the solution matrix X, iykyk. And if XTRUE is the true solution that goes with X(j), then FERR(j) is an estimated upper bound for the magnitude of the largest element in (X(j) - XTRUE) divided by the magnitude of the largest element in X(j). 💅

The tea: this estimate is as reliable as the estimate for RCOND, and honestly it's almost always a slight overestimate of the true error. Overthinking it? Same.

🍓 `BERR`

BERR is a DOUBLE PRECISION array, dimension (NRHS).

This one's the componentwise relative backward error of each solution vector X(j) — like, the smallest relative change in any element of A or B that makes X(j) an exact solution. It's giving "how much would reality have to shift for this to be perfect" energy. ✨

🦋 `WORK`

WORK is a COMPLEX*16 array, dimension (2*N).

That's it. That's the whole vibe. It's just workspace, an icon doing background labor with zero credit. 🫶

🌸 `RWORK`

RWORK is a DOUBLE PRECISION array, dimension (MAX(1,2*N)).

On exit, RWORK(1) contains the reciprocal pivot growth factor norm(A)/norm(U). The norm is used. (no cap, the source text literally has a blank spot right there where the norm symbol used to be — it did not survive the trip 😭)

If RWORK(1) is much less than 1, then the stability of the LU factorization of the (equilibrated) matrix A could be poor. Bestie, listen — this also means that the solution X, condition estimator RCOND, and forward error bound FERR could be unreliable. 🚩 Not a slay.

If factorization fails with 0<INFO<=N, then RWORK(1) contains the reciprocal pivot growth factor for the leading INFO columns of A. Noted and filed away. 📝

👑

INFO is INTEGER, and it's the main character telling you how everything went:

= 0: successful exit. ate. 💅
< 0: if INFO = -i, the i-th argument had an illegal value. 🙈
> 0: if INFO = i, and i is…
- <= N: U(i,i) 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. 💀
- = N+1: U is nonsingular, but RCOND is less than machine precision, meaning that the matrix is singular to working precision. Nevertheless, the solution and error bounds are computed because there are a number of situations where the computed solution can be more accurate than the value of RCOND would suggest. Plot twist, honestly. 🔥

👑 Author

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

⭐ Definition at line 347 of file `zgesvx.f`.

🎀 Author

Generated automatically by Doxygen for LAPACK from the source code. (Yes, there are two Author sections. They said what they said. 🙈)

Alright bestie, that's the whole page — SRC/zgesvx.f(3), straight from LAPACK Version 3.12.0, written by the Univ. of Tennessee / Univ. of California Berkeley / Univ. of Colorado Denver / NAG Ltd. crowd and auto-generated by Doxygen from the actual source code. Go forth and solve responsibly 💖✨🎀

📦 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