This edition of LAPACK introduced new routines and extended the functionality of existing routings. Featured are driver and computational routine for the generalized nonsymmetric eigenproblem, generalized linear least squares problems, and the generalized singular value decomposition.

Lapack Users' Guide

Computers & Technology

Demmel, J.

Programming Languages

Society for Industrial & Applied

<p>ScaLAPACK is an acronym for Scalable Linear Algebra Package or Scalable LAPACK. It is a library of high-performance linear algebra routines for distributed memory message-passing MIMD computers and networks of workstations supporting parallel virtual machine (PVM) and/or message passing interface (MPI). It is a continuation of the LAPACK project, which designed and produced analogous software for workstations, vector supercomputers, and shared memory parallel computers. Both libraries contain routines for solving systems of linear equations, least squares problems, and eigenvalue problems. The goals of both projects are efficiency, scalability, reliability, portability, flexibility, and ease of use. Each Users' Guide includes a CD-ROM containing the HTML version of the ScaLAPACK Users' Guide, the source code for the package, testing and timing programs, prebuilt versions of the library for a number of computers, example programs, and the full set of LAPACK Working Notes.</p>

ScaLAPACK Users' Guide (Software, Environments and Tools, Series Number 4)

Walker, D.

Blackford, L. S.

Choi, J.

Cleary, A.

D'Azevedo, E.

Dhillon, I.

Dongarra, J.

Hammarling, S.

Henry, G.

Petitet, A.

Stanley, K.

Whaley, R. C.

Society for Industrial and Applied Mathematics

<p>LAPACK is a library of numerical linear algebra subroutines designed for high performance on workstations, vector computers, and shared memory multiprocessors. Release 3.0 of LAPACK introduces new routines and extends the functionality of existing routines. The most significant new routines and functions include: 1. a faster singular value decomposition computed by divide-and-conquer 2. faster routines for solving rank-deficient least squares problems: Using QR with column pivoting using the SVD based on divide-and-conquer 3. new routines for the generalized symmetric eigenproblem: faster routines based on divide-and-conquer routines based on bisection/inverse iteration, for computing part of the spectrum 4. faster routine for the symmetric eigenproblem using "relatively robust eigenvector algorithm" 5. new simple and expert drivers for the generalized nonsymmetric eigenproblem, including error bounds 6. solver for generalized Sylvester equation, used in 5 7.computational routines used in 5 Each Users' Guide comes with a 'Quick Reference Guide' card.</p>

LAPACK Users' Guide (Software, Environments and Tools, Series Number 9)

Anderson, E.

Bai, Z.

Bischof, C.

Blackford, S.

Du Croz, J.

Greenbaum, A.

McKenney, A.

Sorensen, D.

Software

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