head 1.3; access; symbols pkgsrc-2019Q4:1.2.0.36 pkgsrc-2019Q4-base:1.2 pkgsrc-2019Q3:1.2.0.32 pkgsrc-2019Q3-base:1.2 pkgsrc-2019Q2:1.2.0.30 pkgsrc-2019Q2-base:1.2 pkgsrc-2019Q1:1.2.0.28 pkgsrc-2019Q1-base:1.2 pkgsrc-2018Q4:1.2.0.26 pkgsrc-2018Q4-base:1.2 pkgsrc-2018Q3:1.2.0.24 pkgsrc-2018Q3-base:1.2 pkgsrc-2018Q2:1.2.0.22 pkgsrc-2018Q2-base:1.2 pkgsrc-2018Q1:1.2.0.20 pkgsrc-2018Q1-base:1.2 pkgsrc-2017Q4:1.2.0.18 pkgsrc-2017Q4-base:1.2 pkgsrc-2017Q3:1.2.0.16 pkgsrc-2017Q3-base:1.2 pkgsrc-2017Q2:1.2.0.12 pkgsrc-2017Q2-base:1.2 pkgsrc-2017Q1:1.2.0.10 pkgsrc-2017Q1-base:1.2 pkgsrc-2016Q4:1.2.0.8 pkgsrc-2016Q4-base:1.2 pkgsrc-2016Q3:1.2.0.6 pkgsrc-2016Q3-base:1.2 pkgsrc-2016Q2:1.2.0.4 pkgsrc-2016Q2-base:1.2 pkgsrc-2016Q1:1.2.0.2 pkgsrc-2016Q1-base:1.2 pkgsrc-2015Q4:1.1.1.1.0.30 pkgsrc-2015Q4-base:1.1.1.1 pkgsrc-2015Q3:1.1.1.1.0.28 pkgsrc-2015Q3-base:1.1.1.1 pkgsrc-2015Q2:1.1.1.1.0.26 pkgsrc-2015Q2-base:1.1.1.1 pkgsrc-2015Q1:1.1.1.1.0.24 pkgsrc-2015Q1-base:1.1.1.1 pkgsrc-2014Q4:1.1.1.1.0.22 pkgsrc-2014Q4-base:1.1.1.1 pkgsrc-2014Q3:1.1.1.1.0.20 pkgsrc-2014Q3-base:1.1.1.1 pkgsrc-2014Q2:1.1.1.1.0.18 pkgsrc-2014Q2-base:1.1.1.1 pkgsrc-2014Q1:1.1.1.1.0.16 pkgsrc-2014Q1-base:1.1.1.1 pkgsrc-2013Q4:1.1.1.1.0.14 pkgsrc-2013Q4-base:1.1.1.1 pkgsrc-2013Q3:1.1.1.1.0.12 pkgsrc-2013Q3-base:1.1.1.1 pkgsrc-2013Q2:1.1.1.1.0.10 pkgsrc-2013Q2-base:1.1.1.1 pkgsrc-2013Q1:1.1.1.1.0.8 pkgsrc-2013Q1-base:1.1.1.1 pkgsrc-2012Q4:1.1.1.1.0.6 pkgsrc-2012Q4-base:1.1.1.1 pkgsrc-2012Q3:1.1.1.1.0.4 pkgsrc-2012Q3-base:1.1.1.1 pkgsrc-2012Q2:1.1.1.1.0.2 pkgsrc-2012Q2-base:1.1.1.1 pkgsrc-base:1.1.1.1 TNF:1.1.1; locks; strict; comment @# @; 1.3 date 2020.03.13.07.48.57; author plunky; state dead; branches; next 1.2; commitid oXgz2iTyxJ2Qgd0C; 1.2 date 2016.03.25.21.08.09; author joerg; state Exp; branches; next 1.1; commitid z6VhuEwq8b3Og40z; 1.1 date 2012.05.29.16.38.01; author asau; state Exp; branches 1.1.1.1; next ; 1.1.1.1 date 2012.05.29.16.38.01; author asau; state Exp; branches; next ; desc @@ 1.3 log @Remove math/arpack successor math/arpack-ng No arpack release has been published by Rice University for many years, and arpack-ng aims to provide a common repository of community fixes with a testsuite. @ text @@@comment $NetBSD: PLIST,v 1.2 2016/03/25 21:08:09 joerg Exp $ lib/libarpack.la @ 1.2 log @Libtoolize to provide shared libraries. Fixes parallel build as side effect. Bump revision. @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.1.1.1 2012/05/29 16:38:01 asau Exp $ @ 1.1 log @Initial revision @ text @d1 2 a2 2 @@comment $NetBSD$ lib/libarpack.a @ 1.1.1.1 log @Import ARPACK 96 as math/arpack. Contributed to pkgsrc-wip by Jason Bacon. ARPACK is a collection of Fortran77 subroutines designed to solve large scale eigenvalue problems. The package is designed to compute a few eigenvalues and corresponding eigenvectors of a general n by n matrix A. It is most appropriate for large sparse or structured matrices A where structured means that a matrix-vector product w <- Av requires order n rather than the usual order n**2 floating point operations. This software is based upon an algorithmic variant of the Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When the matrix A is symmetric it reduces to a variant of the Lanczos process called the Implicitly Restarted Lanczos Method (IRLM). These variants may be viewed as a synthesis of the Arnoldi/Lanczos process with the Implicitly Shifted QR technique that is suitable for large scale problems. For many standard problems, a matrix factorization is not required. Only the action of the matrix on a vector is needed. ARPACK software is capable of solving large scale symmetric, nonsymmetric, and generalized eigenproblems from significant application areas. The software is designed to compute a few (k) eigenvalues with user specified features such as those of largest real part or largest magnitude. Storage requirements are on the order of n*k locations. No auxiliary storage is required. A set of Schur basis vectors for the desired k-dimensional eigen-space is computed which is numerically orthogonal to working precision. Numerically accurate eigenvectors are available on request. Important Features: o Reverse Communication Interface. o Single and Double Precision Real Arithmetic Versions for Symmetric, Non-symmetric, Standard or Generalized Problems. o Single and Double Precision Complex Arithmetic Versions for Standard or Generalized Problems. o Routines for Banded Matrices - Standard or Generalized Problems. o Routines for The Singular Value Decomposition. o Example driver routines that may be used as templates to implement numerous Shift-Invert strategies for all problem types, data types and precision. @ text @@