Revision 1.1.1.1 (vendor branch), Tue May 29 16:38:01 2012 UTC (11 years, 10 months ago) by asau
Branch: TNF
CVS Tags: pkgsrc-base, pkgsrc-2015Q3-base, pkgsrc-2015Q3, pkgsrc-2015Q2-base, pkgsrc-2015Q2, pkgsrc-2015Q1-base, pkgsrc-2015Q1, pkgsrc-2014Q4-base, pkgsrc-2014Q4, pkgsrc-2014Q3-base, pkgsrc-2014Q3, pkgsrc-2014Q2-base, pkgsrc-2014Q2, pkgsrc-2014Q1-base, pkgsrc-2014Q1, pkgsrc-2013Q4-base, pkgsrc-2013Q4, pkgsrc-2013Q3-base, pkgsrc-2013Q3, pkgsrc-2013Q2-base, pkgsrc-2013Q2, pkgsrc-2013Q1-base, pkgsrc-2013Q1, pkgsrc-2012Q4-base, pkgsrc-2012Q4, pkgsrc-2012Q3-base, pkgsrc-2012Q3, pkgsrc-2012Q2-base, pkgsrc-2012Q2
Changes since 1.1: +0 -0 lines

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.