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. 
