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Linear Equations

We use the standard notation for a system of simultaneous linear equations:

A x = b (2.4)

where A is the coefficient matrix, b is the right hand side, and x is the solution. In (2.4) A is assumed to be a square matrix of order n, but some of the individual routines allow A to be rectangular. If there are several right hand sides, we write

A X = B (2.5)

where the columns of B are the individual right hand sides, and the columns of X are the corresponding solutions. The basic task is to compute X, given A and B.

If A is upper or lower triangular, (2.4) can be solved by a straightforward process of backward or forward substitution. Otherwise, the solution is obtained after first factorizing A as a product of triangular matrices (and possibly also a diagonal matrix or permutation matrix).

The form of the factorization depends on the properties of the matrix A. LAPACK provides routines for the following types of matrices, based on the stated factorizations:

The factorization for a general tridiagonal matrix is like that for a general band matrix with kl = 1 and ku = 1. The factorization for a symmetric positive definite band matrix with k superdiagonals (or subdiagonals) has the same form as for a symmetric positive definite matrix, but the factor U (or L) is a band matrix with k superdiagonals (subdiagonals). Band matrices use a compact band storage scheme described in section 5.3.3. LAPACK routines are also provided for symmetric matrices (whether positive definite or indefinite) using packed storage, as described in section 5.3.2.

While the primary use of a matrix factorization is to solve a system of equations, other related tasks are provided as well. Wherever possible, LAPACK provides routines to perform each of these tasks for each type of matrix and storage scheme (see Tables 2.7 and 2.8). The following list relates the tasks to the last 3 characters of the name of the corresponding computational routine:

xyyTRF:
factorize (obviously not needed for triangular matrices);

xyyTRS:
use the factorization (or the matrix A itself if it is triangular) to solve (2.5) by forward or backward substitution;

xyyCON:
estimate the reciprocal of the condition number $\kappa(A) = \Vert A\Vert . \Vert A^{-1} \Vert$ ; Higham's modification [63] of Hager's method [59] is used to estimate |A-1|, except for symmetric positive definite tridiagonal matrices for which it is computed directly with comparable efficiency [61];

xyyRFS:
compute bounds on the error in the computed solution (returned by the xyyTRS routine), and refine the solution to reduce the backward error (see below);

xyyTRI:
use the factorization (or the matrix A itself if it is triangular) to compute A-1 (not provided for band matrices, because the inverse does not in general preserve bandedness);

xyyEQU:
compute scaling factors to equilibrate A (not provided for tridiagonal, symmetric indefinite, or triangular matrices). These routines do not actually scale the matrices: auxiliary routines xLAQyy may be used for that purpose -- see the code of the driver routines xyySVX for sample usage.

Note that some of the above routines depend on the output of others:

xyyTRF:
may work on an equilibrated matrix produced by xyyEQU and xLAQyy, if yy is one of {GE, GB, PO, PP, PB};

xyyTRS:
requires the factorization returned by xyyTRF;

xyyCON:
requires the norm of the original matrix A, and the factorization returned by xyyTRF;

xyyRFS:
requires the original matrices A and B, the factorization returned by xyyTRF, and the solution X returned by xyyTRS;

xyyTRI:
requires the factorization returned by xyyTRF.

The RFS (``refine solution'') routines perform iterative refinement and compute backward and forward error bounds for the solution. Iterative refinement is done in the same precision as the input data. In particular, the residual is not computed with extra precision, as has been traditionally done. The benefit of this procedure is discussed in Section 4.4.


Table 2.7: Computational routines for linear equations
Type of matrix Operation Single precision Double precision
and storage scheme   real complex real complex
general factorize SGETRF CGETRF DGETRF ZGETRF
  solve using factorization SGETRS CGETRS DGETRS ZGETRS
  estimate condition number SGECON CGECON DGECON ZGECON
  error bounds for solution SGERFS CGERFS DGERFS ZGERFS
  invert using factorization SGETRI CGETRI DGETRI ZGETRI
  equilibrate SGEEQU CGEEQU DGEEQU ZGEEQU
general factorize SGBTRF CGBTRF DGBTRF ZGBTRF
band solve using factorization SGBTRS CGBTRS DGBTRS ZGBTRS
  estimate condition number SGBCON CGBCON DGBCON ZGBCON
  error bounds for solution SGBRFS CGBRFS DGBRFS ZGBRFS
  equilibrate SGBEQU CGBEQU DGBEQU ZGBEQU
general factorize SGTTRF CGTTRF DGTTRF ZGTTRF
tridiagonal solve using factorization SGTTRS CGTTRS DGTTRS ZGTTRS
  estimate condition number SGTCON CGTCON DGTCON ZGTCON
  error bounds for solution SGTRFS CGTRFS DGTRFS ZGTRFS
symmetric/Hermitian factorize SPOTRF CPOTRF DPOTRF ZPOTRF
positive definite solve using factorization SPOTRS CPOTRS DPOTRS ZPOTRS
  estimate condition number SPOCON CPOCON DPOCON ZPOCON
  error bounds for solution SPORFS CPORFS DPORFS ZPORFS
  invert using factorization SPOTRI CPOTRI DPOTRI ZPOTRI
  equilibrate SPOEQU CPOEQU DPOEQU ZPOEQU
symmetric/Hermitian factorize SPPTRF CPPTRF DPPTRF ZPPTRF
positive definite solve using factorization SPPTRS CPPTRS DPPTRS ZPPTRS
(packed storage) estimate condition number SPPCON CPPCON DPPCON ZPPCON
  error bounds for solution SPPRFS CPPRFS DPPRFS ZPPRFS
  invert using factorization SPPTRI CPPTRI DPPTRI ZPPTRI
  equilibrate SPPEQU CPPEQU DPPEQU ZPPEQU
symmetric/Hermitian factorize SPBTRF CPBTRF DPBTRF ZPBTRF
positive definite solve using factorization SPBTRS CPBTRS DPBTRS ZPBTRS
band estimate condition number SPBCON CPBCON DPBCON ZPBCON
  error bounds for solution SPBRFS CPBRFS DPBRFS ZPBRFS
  equilibrate SPBEQU CPBEQU DPBEQU ZPBEQU
symmetric/Hermitian factorize SPTTRF CPTTRF DPTTRF ZPTTRF
positive definite solve using factorization SPTTRS CPTTRS DPTTRS ZPTTRS
tridiagonal estimate condition number SPTCON CPTCON DPTCON ZPTCON
  error bounds for solution SPTRFS CPTRFS DPTRFS ZPTRFS


Table 2.8: Computational routines for linear equations (continued)
Type of matrix Operation Single precision Double precision
and storage scheme   real complex real complex
symmetric/Hermitian factorize SSYTRF CHETRF DSYTRF ZHETRF
indefinite solve using factorization SSYTRS CHETRS DSYTRS ZHETRS
  estimate condition number SSYCON CHECON DSYCON ZHECON
  error bounds for solution SSYRFS CHERFS DSYRFS ZHERFS
  invert using factorization SSYTRI CHETRI DSYTRI ZHETRI
complex symmetric factorize   CSYTRF   ZSYTRF
  solve using factorization   CSYTRS   ZSYTRS
  estimate condition number   CSYCON   ZSYCON
  error bounds for solution   CSYRFS   ZSYRFS
  invert using factorization   CSYTRI   ZSYTRI
symmetric/Hermitian factorize SSPTRF CHPTRF DSPTRF ZHPTRF
indefinite solve using factorization SSPTRS CHPTRS DSPTRS ZHPTRS
(packed storage) estimate condition number SSPCON CHPCON DSPCON ZHPCON
  error bounds for solution SSPRFS CHPRFS DSPRFS ZHPRFS
  invert using factorization SSPTRI CHPTRI DSPTRI ZHPTRI
complex symmetric factorize   CSPTRF   ZSPTRF
(packed storage) solve using factorization   CSPTRS   ZSPTRS
  estimate condition number   CSPCON   ZSPCON
  error bounds for solution   CSPRFS   ZSPRFS
  invert using factorization   CSPTRI   ZSPTRI
triangular solve STRTRS CTRTRS DTRTRS ZTRTRS
  estimate condition number STRCON CTRCON DTRCON ZTRCON
  error bounds for solution STRRFS CTRRFS DTRRFS ZTRRFS
  invert STRTRI CTRTRI DTRTRI ZTRTRI
triangular solve STPTRS CTPTRS DTPTRS ZTPTRS
(packed storage) estimate condition number STPCON CTPCON DTPCON ZTPCON
  error bounds for solution STPRFS CTPRFS DTPRFS ZTPRFS
  invert STPTRI CTPTRI DTPTRI ZTPTRI
triangular solve STBTRS CTBTRS DTBTRS ZTBTRS
band estimate condition number STBCON CTBCON DTBCON ZTBCON
  error bounds for solution STBRFS CTBRFS DTBRFS ZTBRFS


next up previous contents index
Next: Orthogonal Factorizations and Linear Up: Computational Routines Previous: Computational Routines   Contents   Index
Susan Blackford
1999-10-01