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<title>pkgsrc/math/arpack, branch TNF</title>
<subtitle>[no description]</subtitle>
<id>https://git.osdyson.ru/mirror/pkgsrc/atom?h=TNF</id>
<link rel='self' href='https://git.osdyson.ru/mirror/pkgsrc/atom?h=TNF'/>
<link rel='alternate' type='text/html' href='https://git.osdyson.ru/mirror/pkgsrc/'/>
<updated>2012-05-29T16:38:01Z</updated>
<entry>
<title>Import ARPACK 96 as math/arpack.</title>
<updated>2012-05-29T16:38:01Z</updated>
<author>
<name>asau</name>
<email>asau@pkgsrc.org</email>
</author>
<published>2012-05-29T16:38:01Z</published>
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<id>urn:sha1:f2cc181b288727c3b7bf041baf5af719623d7403</id>
<content type='text'>
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 &lt;- 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.</content>
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