SRC/DEPRECATED/sgelsx.f(3)

📦
version 3.12.1 · 2 versions
, in arch rolling

💅 NAME

SRC/DEPRECATED/sgelsx.f — bestie, this is the legacy girlie of the LAPACK lineup ✨

🦋 SYNOPSIS

Functions/Subroutines

subroutine sgelsx (m, n, nrhs, a, lda, b, ldb, jpvt, rcond, rank, work,
    info)

SGELSX solves overdetermined or underdetermined systems for GE matrices 👑

🌸 Function/Subroutine Documentation

subroutine sgelsx (integer m, integer n, integer nrhs, real, dimension( lda,
    * ) a, integer lda, real, dimension( ldb, * ) b, integer ldb, integer,
    dimension( * ) jpvt, real rcond, integer rank, real, dimension( * )
    work, integer info)

SGELSX solves overdetermined or underdetermined systems for GE matrices 💖

✨ Purpose:

This routine is deprecated and has been replaced by routine SGELSY. Like, she's retired, she's living her best life, but she's still here okay 😭

SGELSX computes the minimum-norm solution to a real 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.

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. multiple besties at once, we love a multipurpose icon 💅

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 orthogonal 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**T [ inv(T11)*Q1**T*B ]
             [        0         ]

where Q1 consists of the first RANK columns of Q. slay 🎀

🎀 Parameters

  • M — M is INTEGER. The number of rows of the matrix A. M >= 0. no cap 📏
  • 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 matrices B and X. NRHS >= 0. 👑
  • A — A is REAL 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 never forgotten 🫶

  • LDA — LDA is INTEGER. The leading dimension of the array A. LDA >= max(1,M). 🔥
  • B — B is REAL array, dimension (LDB,NRHS).

On entry, the M-by-NRHS right hand side matrix B.

On exit, 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 elements N+1:M in that column. noted, fr fr 💜

  • LDB — LDB is INTEGER. The leading dimension of the array B. LDB >= max(1,M,N). ⭐
  • JPVT — JPVT is INTEGER array, dimension (N).

On entry, if JPVT(i) .ne. 0, the i-th column of A is an initial column, otherwise it is a free column. Before the QR factorization of A, all initial columns are permuted to the leading positions; only the remaining free columns are moved as a result of column pivoting during the factorization. she's got priorities 💅

On exit, if JPVT(i) = k, then the i-th column of A*P was the k-th column of A. receipts! 🧾

  • RCOND — RCOND is 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. the vibe check for rank 🦋
  • RANK — RANK is 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 — WORK is REAL array, dimension
(max( min(M,N)+3*N, 2*min(M,N)+NRHS )),

that's the whole slay allocation ✨

  • INFO — INFO is 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.

Definition at line 176 of file sgelsx.f. 📍

💜 Author

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LAPACK, Version 3.12.0, SRC/DEPRECATED/sgelsx.f(3) — okay that's the whole page bestie, go forth and solve those systems, ily, mwah 🍓🧚‍♀️💜

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