Source: arpack
Section: math
Priority: optional
Build-Depends: debhelper, g77 ( >> 2.95 )
Maintainer: Christophe Prud'homme <prudhomm@mit.edu>
Standards-Version: 3.2.1

Package: arpack2-dev
Architecture: any
Depends: arpack2, lapack-dev, libc6-dev
Recommends: atlas2-base-dev
Description: Fortran77 subroutines to solve large scale eigenvalue problems. 
 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, 
 o 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. 

Package: arpack2
Architecture: any
Depends: lapack, ${shlibs:Depends}
Recommends: atlas2-base
Description: Fortran77 subroutines to solve large scale eigenvalue problems.
 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, 
 o 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. 
