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math/arpack,

*Library of subroutines to solve eigenvalue problems*

**Branch:** pkgsrc-2018Q3,

**Version: **96nb2,

**Package name:** arpack-96nb2,

**Maintainer: **jwbaconARPACK 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.

**Required to run:**[

lang/g95]

### Master sites:

**SHA1:** 3f91de2b39b484bc8365f8048c9eb109e0306e1c

**RMD160:** 3d1c1c307223961506066f895b7ab291861e73fe

**Filesize:** 621.938 KB

### Version history: (Expand)

- (
**2018-10-22**) Package added to pkgsrc.se, version **arpack-96nb2** (created)