complexGEsolve(3)

📦

version 3.11.0 · 2 versions

3.11.0 · debian bookworm (latest)
3.10.0 · ubuntu jammy

🦋 complexGEsolve(3) — LAPACK 🦋

okay bestie, sit DOWN, because this is complexGEsolve(3), and it's basically the entire friend group of complex solve driver functions for GE matrices. not one routine, not two — a whole ensemble cast of them 💅✨

✨ NAME ✨

complexGEsolve — complex

that's it. that's the whole name section. it's giving minimalist. we love that for it 🎀

🎀 SYNOPSIS 🎀

Functions

okay so here's the lineup, no cap 🫶

subroutine cgels (TRANS, M, N, NRHS, A, LDA, B, LDB, WORK, LWORK, INFO) — CGELS solves overdetermined or underdetermined systems for GE matrices
subroutine cgelsd (M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, RWORK, IWORK, INFO) — CGELSD computes the minimum-norm solution to a linear least squares problem for GE matrices
subroutine cgelss (M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, LWORK, RWORK, INFO) — CGELSS solves overdetermined or underdetermined systems for GE matrices
subroutine cgelst (TRANS, M, N, NRHS, A, LDA, B, LDB, WORK, LWORK, INFO) — CGELST solves overdetermined or underdetermined systems for GE matrices using QR or LQ factorization with compact WY representation of Q.
subroutine cgelsy (M, N, NRHS, A, LDA, B, LDB, JPVT, RCOND, RANK, WORK, LWORK, RWORK, INFO) — CGELSY solves overdetermined or underdetermined systems for GE matrices
subroutine cgesv (N, NRHS, A, LDA, IPIV, B, LDB, INFO) — CGESV computes the solution to system of linear equations A * X = B for GE matrices (simple driver)
subroutine cgesvx (FACT, TRANS, N, NRHS, A, LDA, AF, LDAF, IPIV, EQUED, R, C, B, LDB, X, LDX, RCOND, FERR, BERR, WORK, RWORK, INFO) — CGESVX computes the solution to system of linear equations A * X = B for GE matrices
subroutine cgesvxx (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, RWORK, INFO) — CGESVXX computes the solution to system of linear equations A * X = B for GE matrices
subroutine cgetsls (TRANS, M, N, NRHS, A, LDA, B, LDB, WORK, LWORK, INFO) — CGETSLS

look at that roster. iconic behavior, honestly 👑

💖 Detailed Description 💖

This is the group of complex solve driver functions for GE matrices

that's the whole description bestie. they said "the vibe is the description" and left. periodt 💀

🧚 Function Documentation 🧚

🌸 subroutine cgels (character TRANS, integer M, integer N, integer NRHS, complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldb, * ) B, integer LDB, complex, dimension( * ) WORK, integer LWORK, integer INFO) 🌸

CGELS solves overdetermined or underdetermined systems for GE matrices

⭐ Purpose:

CGELS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A. and the whole thing assumes A has full rank. no rank-deficient chaos here, we're keeping it clean ✨

here are the options, bestie, all four of them:

If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A*X ||.

If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B.
If TRANS = 'C' and m >= n: find the minimum norm solution of an underdetermined system A**H * X = B.
If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A**H * X ||.

Several right hand side vectors b and solution vectors x can be handled in a single call — they're stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. one call, many vectors, multitasking queen 🫶

⭐ Parameters

TRANS — TRANS is CHARACTER*1
- = 'N': the linear system involves A;
- = 'C': the linear system involves A**H.
M — M is INTEGER The number of rows of the matrix A. M >= 0.
N — N is INTEGER The number of columns 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 array, dimension (LDA,N) On entry, the M-by-N matrix A.
- if M >= N, A is overwritten by details of its QR factorization as returned by CGEQRF;
- if M < N, A is overwritten by details of its LQ factorization as returned by CGELQF.
LDA — LDA is INTEGER The leading dimension of the array A. LDA >= max(1,M).
B — B is COMPLEX array, dimension (LDB,NRHS) On entry, the matrix B of right hand side vectors, stored columnwise; B is M-by-NRHS if TRANS = 'N', or N-by-NRHS if TRANS = 'C'. On exit, if INFO = 0, B is overwritten by the solution vectors, stored columnwise:
- if TRANS = 'N' and m >= n, rows 1 to n of B contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of the modulus of elements N+1 to M in that column;
- if TRANS = 'N' and m < n, rows 1 to N of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m >= n, rows 1 to M of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m < n, rows 1 to M of B contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of the modulus of elements M+1 to N in that column.
LDB — LDB is INTEGER The leading dimension of the array B. LDB >= MAX(1,M,N).
WORK — WORK is COMPLEX array, dimension (MAX(1,LWORK)) On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
LWORK — LWORK is INTEGER The dimension of the array WORK. LWORK >= max( 1, MN + max( MN, NRHS ) ). For optimal performance, LWORK >= max( 1, MN + max( MN, NRHS )*NB ). where MN = min(M,N) and NB is the optimum block size.

If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA. sneaky lil shortcut, love that 💅

INFO — INFO is INTEGER
- = 0: successful exit
- < 0: if INFO = -i, the i-th argument had an illegal value
- > 0: if INFO = i, the i-th diagonal element of the triangular factor of A is zero, so that A does not have full rank; the least squares solution could not be computed.

⭐ Author

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

🌸 subroutine cgelsd (integer M, integer N, integer NRHS, complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldb, * ) B, integer LDB, real, dimension( * ) S, real RCOND, integer RANK, complex, dimension( * ) WORK, integer LWORK, real, dimension( * ) RWORK, integer, dimension( * ) IWORK, integer INFO) 🌸

CGELSD computes the minimum-norm solution to a linear least squares problem for GE matrices

⭐ Purpose:

CGELSD computes the minimum-norm solution to a real linear least squares problem:

minimize 2-norm(| b - A*x |)

using the singular value decomposition (SVD) of A. A is an M-by-N matrix which may be rank-deficient — yes bestie, unlike CGELS, this one is built different and handles the messy case 😌

Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.

The problem is solved in three steps:

Reduce the coefficient matrix A to bidiagonal form with Householder transformations, reducing the original problem into a 'bidiagonal least squares problem' (BLS)
Solve the BLS using a divide and conquer approach.
Apply back all the Householder transformations to solve the original least squares problem.

The effective rank of A is determined by treating as zero those singular values which are less than RCOND times the largest singular value.

The divide and conquer algorithm makes very mild assumptions about floating point arithmetic. It will work on machines with a guard digit in add/subtract, or on those binary machines without guard digits which subtract like the Cray X-MP, Cray Y-MP, Cray C-90, or Cray-2. It could conceivably fail on hexadecimal or decimal machines without guard digits, but we know of none. (they really said "we know of none" — the confidence, the audacity, iconic 💀)

⭐ Parameters

M — M is INTEGER The number of rows of the matrix A. M >= 0.
N — N is INTEGER The number of columns 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 array, dimension (LDA,N) On entry, the M-by-N matrix A. On exit, A has been destroyed. (rip A, you served 💀🫡)
LDA — LDA is INTEGER The leading dimension of the array A. LDA >= max(1,M).
B — B is COMPLEX array, dimension (LDB,NRHS) On entry, the M-by-NRHS right hand side matrix B. On exit, B is overwritten by the N-by-NRHS solution matrix X. If m >= n and RANK = n, the residual sum-of-squares for the solution in the i-th column is given by the sum of squares of the modulus of elements n+1:m in that column.
LDB — LDB is INTEGER The leading dimension of the array B. LDB >= max(1,M,N).
S — S is REAL array, dimension (min(M,N)) The singular values of A in decreasing order. The condition number of A in the 2-norm = S(1)/S(min(m,n)).
RCOND — RCOND is REAL RCOND is used to determine the effective rank of A. Singular values S(i) <= RCOND*S(1) are treated as zero. If RCOND < 0, machine precision is used instead.
RANK — RANK is INTEGER The effective rank of A, i.e., the number of singular values which are greater than RCOND*S(1).
WORK

and that's exactly where part one cuts off bestie — WORK is standing there mid-introduction like 👁️👄👁️ …

WORK — COMPLEX array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, then WORK(1) hands you back the optimal LWORK. cute, right? 💅
LWORK — INTEGER, the dimension of the array WORK. LWORK must be at least 1. The exact minimum amount of workspace you need depends on M, N and NRHS. As long as LWORK is at least

2 * N + N * NRHS

if M is greater than or equal to N, or

2 * M + M * NRHS

if M is less than N, the code will execute correctly. For good performance, LWORK should generally be larger — she's not like other girls, she wants room to breathe. ✨

If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the array WORK and the minimum sizes of the arrays RWORK and IWORK, and returns these values as the first entries of the WORK, RWORK and IWORK arrays, and no error message related to LWORK is issued by XERBLA. no cap. 🫶

RWORK — REAL array, dimension (MAX(1,LRWORK)).

LRWORK >=
   10*N + 2*N*SMLSIZ + 8*N*NLVL + 3*SMLSIZ*NRHS +
   MAX( (SMLSIZ+1)**2, N*(1+NRHS) + 2*NRHS )

if M is greater than or equal to N or

LRWORK >=
   10*M + 2*M*SMLSIZ + 8*M*NLVL + 3*SMLSIZ*NRHS +
   MAX( (SMLSIZ+1)**2, N*(1+NRHS) + 2*NRHS )

if M is less than N, the code will execute correctly. SMLSIZ is returned by ILAENV and is equal to the maximum size of the subproblems at the bottom of the computation tree (usually about 25), and

NLVL = MAX( 0, INT( LOG_2( MIN( M,N )/(SMLSIZ+1) ) ) + 1 )

On exit, if INFO = 0, RWORK(1) returns the minimum LRWORK. 🌸

IWORK — INTEGER array, dimension (MAX(1,LIWORK)). LIWORK >= max(1, 3*MINMN*NLVL + 11*MINMN), where MINMN = MIN( M,N ). On exit, if INFO = 0, IWORK(1) returns the minimum LIWORK. it's giving "I did the math so you don't have to". 💖
INFO — INTEGER.
- = 0: successful exit 🎉
- < 0: if INFO = -i, the i-th argument had an illegal value. rude of you, but ok 🙈
- > 0: the algorithm for computing the SVD failed to converge; if INFO = i, i off-diagonal elements of an intermediate bidiagonal form did not converge to zero. 😭

💫 Author 💫

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🌈 Contributors: 🌈

Ming Gu and Ren-Cang Li, Computer Science Division, University of California at Berkeley, USA

Osni Marques, LBNL/NERSC, USA

👑 subroutine `cgelss` 👑

subroutine cgelss (integer M, integer N, integer NRHS, complex, dimension(
    lda, * ) A, integer LDA, complex, dimension( ldb, * ) B, integer LDB,
    real, dimension( * ) S, real RCOND, integer RANK, complex, dimension( *
    ) WORK, integer LWORK, real, dimension( * ) RWORK, integer INFO)

CGELSS solves overdetermined or underdetermined systems for GE matrices — okay bestie, this is the SVD one, the main character of least squares. 🔥

✨ Purpose: ✨

CGELSS computes the minimum norm solution to a complex linear least squares problem:

Minimize 2-norm(| b - A*x |).

using the singular value decomposition (SVD) of A. A is an M-by-N matrix which may be rank-deficient.

Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.

The effective rank of A is determined by treating as zero those singular values which are less than RCOND times the largest singular value. so basically: tiny singular values get ghosted. 💀

🌸 Parameters 🌸

M — INTEGER. The number of rows of the matrix A. M >= 0.
N — INTEGER. The number of columns of the matrix A. N >= 0.
NRHS — INTEGER. The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0.
A — COMPLEX array, dimension (LDA,N). On entry, the M-by-N matrix A. On exit, the first min(m,n) rows of A are overwritten with its right singular vectors, stored rowwise. she's transformed, periodt. ✨
LDA — INTEGER. The leading dimension of the array A. LDA >= max(1,M).
B — COMPLEX array, dimension (LDB,NRHS). On entry, the M-by-NRHS right hand side matrix B. On exit, B is overwritten by the N-by-NRHS solution matrix X. If m >= n and RANK = n, the residual sum-of-squares for the solution in the i-th column is given by the sum of squares of the modulus of elements n+1:m in that column.
LDB — INTEGER. The leading dimension of the array B. LDB >= max(1,M,N).
S — REAL array, dimension (min(M,N)). The singular values of A in decreasing order. The condition number of A in the 2-norm = S(1)/S(min(m,n)).
RCOND — REAL. RCOND is used to determine the effective rank of A. Singular values S(i) <= RCOND*S(1) are treated as zero. If RCOND < 0, machine precision is used instead.
RANK — INTEGER. The effective rank of A, i.e., the number of singular values which are greater than RCOND*S(1).
WORK — COMPLEX array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
LWORK — INTEGER. The dimension of the array WORK. LWORK >= 1, and also:

LWORK >=  2*min(M,N) + max(M,N,NRHS)

For good performance, LWORK should generally be larger.

If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA. 💅

RWORK — REAL array, dimension (5*min(M,N)).
INFO — INTEGER.
- = 0: successful exit 🎀
- < 0: if INFO = -i, the i-th argument had an illegal value.
- > 0: the algorithm for computing the SVD failed to converge; if INFO = i, i off-diagonal elements of an intermediate bidiagonal form did not converge to zero. 😭

💜 Author 💜

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🦋 subroutine `cgelst` 🦋

subroutine cgelst (character TRANS, integer M, integer N, integer NRHS,
    complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldb, *
    ) B, integer LDB, complex, dimension( * ) WORK, integer LWORK, integer
    INFO)

CGELST solves overdetermined or underdetermined systems for GE matrices using QR or LQ factorization with compact WY representation of Q. the compact WY representation is the moment, honestly. 👑

✨ Purpose: ✨

CGELST solves overdetermined or underdetermined real linear systems involving an M-by-N matrix A, or its conjugate-transpose, using a QR or LQ factorization of A with compact WY representation of Q. It is assumed that A has full rank. no rank-deficiency allowed, she's booked and busy. 🫶

The following options are provided:

If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A*X ||.

If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B.
If TRANS = 'C' and m >= n: find the minimum norm solution of an underdetermined system A**T * X = B.
If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A**T * X ||.

🌈 Parameters 🌈

TRANS — CHARACTER*1.
- = 'N': the linear system involves A;
- = 'C': the linear system involves A**H.
M — INTEGER. The number of rows of the matrix A. M >= 0.
N — INTEGER. The number of columns of the matrix A. N >= 0.
NRHS — INTEGER. The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0.
A — COMPLEX array, dimension (LDA,N). On entry, the M-by-N matrix A. On exit,
- if M >= N, A is overwritten by details of its QR factorization as returned by CGEQRT;
- if M < N, A is overwritten by details of its LQ factorization as returned by CGELQT.
LDA — INTEGER. The leading dimension of the array A. LDA >= max(1,M).
B — COMPLEX array, dimension (LDB,NRHS). On entry, the matrix B of right hand side vectors, stored columnwise; B is M-by-NRHS if TRANS = 'N', or N-by-NRHS if TRANS = 'C'. On exit, if INFO = 0, B is overwritten by the solution vectors, stored columnwise:
- if TRANS = 'N' and m >= n, rows 1 to n of B contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of modulus of elements N+1 to M in that column;
- if TRANS = 'N' and m < n, rows 1 to N of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m >= n, rows 1 to M of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m < n, rows 1 to M of B contain the least squares solution vectors; the residual sum of squares for the solution in each column is given by the sum of squares of the modulus of elements M+1 to N in that column.
LDB — INTEGER. The leading dimension of the array B. LDB >= MAX(1,M,N).
WORK —
WORK — it's a COMPLEX array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, then WORK(1) hands you back the optimal LWORK. bestie it's literally telling you what it wanted 🫶
LWORK — INTEGER, the dimension of the array WORK. So like, here's the requirement:

LWORK >= max( 1, MN + max( MN, NRHS ) ).

and if you want it to go fast fast (optimal performance, slay):

LWORK >= max( 1, (MN + max( MN, NRHS ))*NB ).

where MN = min(M,N) and NB is the optimum block size. yes queen we love a block size 💅

INFO — INTEGER, the vibe report:
- = 0: successful exit 🎉
- < 0: if INFO = -i, the i-th argument had an illegal value 😭
- > 0: if INFO = i, the i-th diagonal element of the triangular factor of A is zero, so that A does not have full rank; the least squares solution could not be computed. 💀

👑 Author

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🌸 Contributors:

November 2022, Igor Kozachenko, Computer Science Division, University of California, Berkeley

🦋 subroutine `cgelsy`

okay so this one is the main character of the least-squares world. signature first, exactly as it lives:

subroutine cgelsy (integer M, integer N, integer NRHS, complex, dimension(
    lda, * ) A, integer LDA, complex, dimension( ldb, * ) B, integer LDB,
    integer, dimension( * ) JPVT, real RCOND, integer RANK, complex,
    dimension( * ) WORK, integer LWORK, real, dimension( * ) RWORK, integer
    INFO)

CGELSY solves overdetermined or underdetermined systems for GE matrices. no cap. 💖

✨ Purpose:

CGELSY computes the minimum-norm solution to a complex linear least squares problem:

minimize || A * X - B ||

using a complete orthogonal factorization of A. A is an M-by-N matrix which may be rank-deficient. yes it can be down bad and still get solved, we support that 🫶

Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.

The routine first computes a QR factorization with column pivoting:

A * P = Q * [ R11 R12 ]
            [  0  R22 ]

with R11 defined as the largest leading submatrix whose estimated condition number is less than 1/RCOND. The order of R11, RANK, is the effective rank of A. ✨

Then, R22 is considered to be negligible, and R12 is annihilated by unitary transformations from the right, arriving at the complete orthogonal factorization:

A * P = Q * [ T11 0 ] * Z
            [  0  0 ]

The minimum-norm solution is then

X = P * Z**H [ inv(T11)*Q1**H*B ]
             [        0         ]

where Q1 consists of the first RANK columns of Q. okay that's a lot of letters but trust the process 💅

This routine is basically identical to the original xGELSX except three differences:

The permutation of matrix B (the right hand side) is faster and more simple. 💨
The call to the subroutine xGEQPF has been substituted by the the call to the subroutine xGEQP3. This subroutine is a Blas-3 version of the QR factorization with column pivoting. 🔥
Matrix B (the right hand side) is updated with Blas-3.

💜 Parameters

M — INTEGER, the number of rows of the matrix A. M >= 0.
N — INTEGER, the number of columns of the matrix A. N >= 0.
NRHS — INTEGER, the number of right hand sides, i.e., the number of columns of matrices B and X. NRHS >= 0.
A — COMPLEX array, dimension (LDA,N). On entry, the M-by-N matrix A. On exit, A has been overwritten by details of its complete orthogonal factorization. gone but not forgotten 🙈
LDA — INTEGER, the leading dimension of the array A. LDA >= max(1,M).
B — COMPLEX array, dimension (LDB,NRHS). On entry, the M-by-NRHS right hand side matrix B. On exit, the N-by-NRHS solution matrix X. glow up ✨
LDB — INTEGER, the leading dimension of the array B. LDB >= max(1,M,N).
JPVT — INTEGER array, dimension (N). On entry, if JPVT(i) .ne. 0, the i-th column of A is permuted to the front of AP, otherwise column i is a free column. On exit, if JPVT(i) = k, then the i-th column of A*P was the k-th column of A.
RCOND — REAL. RCOND is used to determine the effective rank of A, which is defined as the order of the largest leading triangular submatrix R11 in the QR factorization with pivoting of A, whose estimated condition number < 1/RCOND.
RANK — INTEGER, the effective rank of A, i.e., the order of the submatrix R11. This is the same as the order of the submatrix T11 in the complete orthogonal factorization of A.
WORK — COMPLEX array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
LWORK — INTEGER, the dimension of the array WORK. The unblocked strategy requires that:

LWORK >= MN + MAX( 2*MN, N+1, MN+NRHS )

where MN = min(M,N). The block algorithm requires that:

LWORK >= MN + MAX( 2*MN, NB*(N+1), MN+MN*NB, MN+NB*NRHS )

where NB is an upper bound on the blocksize returned by ILAENV for the routines CGEQP3, CTZRZF, CTZRQF, CUNMQR, and CUNMRZ.

RWORK — REAL array, dimension (2*N).
INFO — INTEGER:
- = 0: successful exit 🎀
- < 0: if INFO = -i, the i-th argument had an illegal value 😭

👑 Author

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🌸 Contributors:

A. Petitet, Computer Science Dept., Univ. of Tenn., Knoxville, USA

E. Quintana-Orti, Depto. de Informatica, Universidad Jaime I, Spain

G. Quintana-Orti, Depto. de Informatica, Universidad Jaime I, Spain

🎀 subroutine `cgesv`

the simple driver era. signature:

subroutine cgesv (integer N, integer NRHS, complex, dimension( lda, * ) A,
    integer LDA, integer, dimension( * ) IPIV, complex, dimension( ldb, * )
    B, integer LDB, integer INFO)

CGESV computes the solution to system of linear equations A * X = B for GE matrices (simple driver). it's giving "I do one thing and I do it well" 💅

✨ Purpose:

CGESV computes 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.

The LU decomposition with partial pivoting and row interchanges is used to factor A as

A = P * L * U,

where P is a permutation matrix, L is unit lower triangular, and U is upper triangular. The factored form of A is then used to solve the system of equations A * X = B.

💜 Parameters

N — INTEGER, the number of linear equations, i.e., the order of the matrix A. N >= 0.
NRHS — INTEGER, the number of right hand sides, i.e., the number of columns of the matrix B. NRHS >= 0.
A — COMPLEX array, dimension (LDA,N). On entry, the N-by-N coefficient matrix A. On exit, the factors L and U from the factorization A = P*L*U; the unit diagonal elements of L are not stored. lowkey they're implied, we don't need receipts ✨
LDA — INTEGER, the leading dimension of the array A. LDA >= max(1,N).
IPIV — INTEGER array, dimension (N). The pivot indices that define the permutation matrix P; row i of the matrix was interchanged with row IPIV(i).
B — COMPLEX array, dimension (LDB,NRHS). On entry, the N-by-NRHS matrix of right hand side matrix B. On exit, if INFO = 0, the N-by-NRHS solution matrix X. 🦋
LDB — INTEGER, the leading dimension of the array B. LDB >= max(1,N).
INFO — INTEGER:
- = 0: successful exit 🎉
- < 0: if INFO = -i, the i-th argument had an illegal value 😭
- > 0: if INFO = i, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution could not be computed. 💀

👑 Author

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

🌈 subroutine `cgesvx`

the expert driver has entered the chat. signature:

subroutine cgesvx (character FACT, character TRANS, integer N, integer NRHS,
    complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldaf, *
    ) AF, integer LDAF, integer, dimension( * ) IPIV, character EQUED, real,
    dimension( * ) R, real, dimension( * ) C, complex, dimension( ldb, * )
    B, integer LDB, complex, dimension( ldx, * ) X, integer LDX, real RCOND,
    real, dimension( * ) FERR, real, dimension( * ) BERR, complex,
    dimension( * ) WORK, real, dimension( * ) RWORK, integer INFO)

CGESVX computes the solution to system of linear equations A * X = B for GE matrices. bestie this one comes with receipts 📋

✨ Purpose:

CGESVX 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. the overachiever behaviour, we're obsessed 🫶

🌸 Description:

The following steps are performed:

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.

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 🎀

okay bestie, here's the whole cast of characters. every single one. buckle up. 🦋

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.

= '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 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 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 CGETRF. 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 CGETRF; 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.

= '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 REAL 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 REAL 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 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 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 REAL

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 is REAL array, dimension (NRHS)

The estimated forward error bound for each solution vector X(j) (the j-th column of the solution matrix X). If XTRUE is the true solution corresponding to X(j), 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 estimate is as reliable as the estimate for RCOND, and is almost always a slight overestimate of the true error.

BERR — BERR is REAL array, dimension (NRHS)

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).

WORK — WORK is COMPLEX array, dimension (2*N)
RWORK — RWORK is REAL array, dimension (2*N)

On exit, RWORK(1) contains the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is used. If RWORK(1) is much less than 1, then the stability of the LU factorization of the (equilibrated) matrix A could be poor. This also means that the solution X, condition estimator RCOND, and forward error bound FERR could be unreliable. If factorization fails with 0<INFO<=N, then RWORK(1) contains the reciprocal pivot growth factor for the leading INFO columns of A.

INFO

bestie the page literally cuts off right there 😭 the rest of INFO is in the next part, keep scrolling 💖

INFO is INTEGER
- = 0: successful exit 🎉
- < 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. 🫠

✨ Author ✨

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

🌟 subroutine cgesvxx 🌟

subroutine cgesvxx (character FACT, character TRANS, integer N, integer
    NRHS, complex, dimension( lda, * ) A, integer LDA, complex, dimension(
    ldaf, * ) AF, integer LDAF, integer, dimension( * ) IPIV, character
    EQUED, real, dimension( * ) R, real, dimension( * ) C, complex,
    dimension( ldb, * ) B, integer LDB, complex, dimension( ldx , * ) X,
    integer LDX, real RCOND, real RPVGRW, real, dimension( * ) BERR, integer
    N_ERR_BNDS, real, dimension( nrhs, * ) ERR_BNDS_NORM, real, dimension(
    nrhs, * ) ERR_BNDS_COMP, integer NPARAMS, real, dimension( * ) PARAMS,
    complex, dimension( * ) WORK, real, dimension( * ) RWORK, integer INFO)

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

🎯 Purpose

CGESVXX 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. 💅

If requested, both normwise and maximum componentwise error bounds are returned. CGESVXX 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. 🔥

CGESVXX 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 CGESVXX 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 CGESVXX would itself produce. 🫶

🛠️ Description

The following steps are performed:

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

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. 🧚

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. 😤
The system of equations is solved for X using the factored form of A. ✨
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. 💯
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. 🎀

📋 Parameters

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. 🤔
- = '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 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 is INTEGER — The number of linear equations, i.e., the order of the matrix A. N >= 0. 🔢
NRHS is INTEGER — The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0. 📚
A is COMPLEX 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 is INTEGER — The leading dimension of the array A. LDA >= max(1,N). 📏
AF is COMPLEX 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 CGETRF. 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 is INTEGER — The leading dimension of the array AF. LDAF >= max(1,N). 📐
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 CGETRF; 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 is CHARACTER*1 — 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 is REAL 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. 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 is a REAL 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 isn't accessed at all. C is an input argument if FACT = 'F'; otherwise C is an output argument. And if FACT = 'F' and EQUED = 'C' or 'B', every single element of C has to be positive. Periodt.

If C is output, each element of C is a power of the radix. If C is input, each element of C should be a power of the radix to make sure 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 isn't equivalent to the input matrix, which then produces error estimates that may not be reliable. 💅

`B`

B is a COMPLEX 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. It's the leading dimension of the array B, and the vibe check is: LDB >= max(1,N). ✅

`X`

X is a COMPLEX array, dimension (LDX,NRHS). If INFO = 0, this is the N-by-NRHS solution matrix X to the original system of equations. Heads up though, bestie: 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. Requirement, no cap: LDX >= max(1,N). ✨

`RCOND`

RCOND is REAL — the reciprocal scaled condition number. It's an estimate of the reciprocal Skeel condition number of the matrix A after equilibration (if equilibration happened). If this is less than the machine precision (and especially if it's zero), then the matrix is singular to working precision. Note that the error may still be small even if this number is super tiny and the matrix looks ill-conditioned. 😭

`RPVGRW`

RPVGRW is REAL — reciprocal pivot growth. On exit, it holds the reciprocal pivot growth factor norm(A)/norm(U). The 'max absolute element' norm is what's used here. 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 all be unreliable. If factorization fails with 0<INFO<=N, then this holds the reciprocal pivot growth factor for the leading INFO columns of A. In CGESVX, this quantity is returned in WORK(1). 👑

`BERR`

BERR is a REAL array, dimension (NRHS) — componentwise relative backward error. Specifically, it's 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 — 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, bestie. 👀

`ERR_BNDS_NORM`

ERR_BNDS_NORM is a REAL array, dimension (NRHS, N_ERR_BNDS). For each right-hand side, this array holds 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 are currently 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 — 'Trust/don't trust' boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * slamch('Epsilon').
err = 2 — 'Guaranteed' 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 'guaranteed'. 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 a REAL array, dimension (NRHS, N_ERR_BNDS). For each right-hand side, this array holds info about various 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))

The array is indexed by the right-hand side i (since the componentwise relative error depends on it), and by the type of error information 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 — 'Trust/don't trust' boolean. Trust the answer if the reciprocal condition number is less than the threshold sqrt(n) * slamch('Epsilon').
err = 2 — 'Guaranteed' 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 'guaranteed'. 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. 📓

`NPARAMS`

NPARAMS is INTEGER — it specifies the number of parameters set in PARAMS. If it's <= 0, then the PARAMS array is never referenced and default values are used instead. 🫶

`PARAMS`

PARAMS is a REAL array, dimension NPARAMS — it specifies the algorithm parameters. If an entry is < 0.0, then that entry gets 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: 1.0
- = 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.
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 array, dimension (2*N).

`RWORK`

RWORK is a REAL array, dimension (2*N).

`INFO`

okay bestie, this one's whole thing is literally right around the corner in the next part — hold my hand, we're almost there 💜

INFO is INTEGER — okay so this is the tea on how the call went, bestie. 👑

= 0: Successful exit. The solution to every right-hand side is guaranteed. ✨
< 0: If INFO = -i, the i-th argument had an illegal value. 🚩
> 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. 💀
= 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. 🔍

👑 Author 👑

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

   subroutine cgetsls (character TRANS, integer M, integer N, integer NRHS,
       complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldb, *
       ) B, integer LDB, complex, dimension( * ) WORK, integer LWORK, integer
       INFO)

🦋 CGETSLS 🦋

🎯 Purpose 🎯

CGETSLS solves overdetermined or underdetermined complex linear systems involving an M-by-N matrix A, using a tall skinny QR or short wide LQ factorization of A. It is assumed that A has full rank. No cap. 💅

The following options are provided:

If TRANS = 'N' and m >= n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A*X ||.

If TRANS = 'N' and m < n: find the minimum norm solution of an underdetermined system A * X = B.
If TRANS = 'C' and m >= n: find the minimum norm solution of an undetermined system A**T * X = B.
If TRANS = 'C' and m < n: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

minimize || B - A**T * X ||.

Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X. 🫶

🌈 Parameters 🌈

TRANS — TRANS is CHARACTER*1
- = 'N': the linear system involves A;
- = 'C': the linear system involves A**H.
M — M is INTEGER. The number of rows of the matrix A. M >= 0.
N — N is INTEGER. The number of columns 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 array, dimension (LDA,N). On entry, the M-by-N matrix A. On exit, A is overwritten by details of its QR or LQ factorization as returned by CGEQR or CGELQ.
LDA — LDA is INTEGER. The leading dimension of the array A. LDA >= max(1,M).
B — B is COMPLEX array, dimension (LDB,NRHS). On entry, the matrix B of right hand side vectors, stored columnwise; B is M-by-NRHS if TRANS = 'N', or N-by-NRHS if TRANS = 'C'. On exit, if INFO = 0, B is overwritten by the solution vectors, stored columnwise:
- if TRANS = 'N' and m >= n, rows 1 to n of B contain the least squares solution vectors.
- if TRANS = 'N' and m < n, rows 1 to N of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m >= n, rows 1 to M of B contain the minimum norm solution vectors;
- if TRANS = 'C' and m < n, rows 1 to M of B contain the least squares solution vectors.
LDB — LDB is INTEGER. The leading dimension of the array B. LDB >= MAX(1,M,N).
WORK — (workspace) COMPLEX array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) contains optimal (or either minimal or optimal, if query was assumed) LWORK. See LWORK for details.
LWORK — LWORK is INTEGER. The dimension of the array WORK. If LWORK = -1 or -2, then a workspace query is assumed. If LWORK = -1, the routine calculates optimal size of WORK for the optimal performance and returns this value in WORK(1). If LWORK = -2, the routine calculates minimal size of WORK and returns this value in WORK(1).
INFO — INFO is INTEGER
- = 0: successful exit
- < 0: if INFO = -i, the i-th argument had an illegal value
- > 0: if INFO = i, the i-th diagonal element of the triangular factor of A is zero, so that A does not have full rank; the least squares solution could not be computed.

💜 Author 💜

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

📝 Author 📝

Generated automatically by Doxygen for LAPACK from the source code.

And that's the whole page, bestie! Version 3.11.0, dated Sun Nov 27 2022, complexGEsolve(3). You ate that. 💅✨

📦 where this page comes from

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complexGEsolve(3)

🦋 complexGEsolve(3) — LAPACK 🦋

✨ NAME ✨

🎀 SYNOPSIS 🎀

Functions

💖 Detailed Description 💖

🧚 Function Documentation 🧚

🌸 subroutine cgels (character TRANS, integer M, integer N, integer NRHS, complex, dimension( lda, * ) A, integer LDA, complex, dimension( ldb, * ) B, integer LDB, complex, dimension( * ) WORK, integer LWORK, integer INFO) 🌸

⭐ Purpose:

⭐ Parameters

⭐ Author

⭐ Purpose:

⭐ Parameters

💫 Author 💫

🌈 Contributors: 🌈

👑 subroutine cgelss 👑

✨ Purpose: ✨

🌸 Parameters 🌸

💜 Author 💜

🦋 subroutine cgelst 🦋

✨ Purpose: ✨

🌈 Parameters 🌈

👑 Author

🌸 Contributors:

🦋 subroutine cgelsy

✨ Purpose:

💜 Parameters

👑 Author

🌸 Contributors:

🎀 subroutine cgesv

✨ Purpose:

💜 Parameters

👑 Author

🌈 subroutine cgesvx

✨ Purpose:

🌸 Description:

🎀 Parameters 🎀

✨ Author ✨

🌟 subroutine cgesvxx 🌟

🎯 Purpose

🛠️ Description

📋 Parameters

C

B

LDB

X

LDX

RCOND

RPVGRW

BERR

N_ERR_BNDS

ERR_BNDS_NORM

ERR_BNDS_COMP

NPARAMS

PARAMS

WORK

RWORK

INFO

👑 Author 👑

🦋 CGETSLS 🦋

🎯 Purpose 🎯

🌈 Parameters 🌈

💜 Author 💜

📝 Author 📝

👑 subroutine `cgelss` 👑

🦋 subroutine `cgelst` 🦋

🦋 subroutine `cgelsy`

🎀 subroutine `cgesv`

🌈 subroutine `cgesvx`

`C`

`B`

`LDB`

`X`

`LDX`

`RCOND`

`RPVGRW`

`BERR`

`N_ERR_BNDS`

`ERR_BNDS_NORM`

`ERR_BNDS_COMP`

`NPARAMS`

`PARAMS`

`WORK`

`RWORK`

`INFO`