slap - methods for the iterative solution of large sparse linear systems
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SLAP This is the official (updated) release version 2.0  of the Sparse
     Linear Algebra  Package: a SLAP  for  the   Masses!   It contains
     "core"  routines   for the    iterative solution  symmetric   and
     non-symmetric positive definite and positive semi-definite linear
     systems.  Included in   this package are  core   routines   to do
     Iterative Refinement iteration, Preconditioned Conjugate Gradient
     iteration, Preconditioned  Conjugate Gradient  iteration   on the
     Normal Equations, Preconditioned BiConjugate  Gradient iteration,
     Preconditioned  BiConjugate Gradient Squared iteration,  Orthomin
     iteration   and  Generalized Minimum    Residual iteration.  Core
     routines  require  the  user   to supply "MATVEC"  (Matrix Vector
     Multiply) and "MSOLVE" (Preconditiong) routines.  This allows the
     core routines to be written in a way  that makes them independent
     of the  matrix data  structure.  For each  core routine there are
     several drivers  and  support  routines  that allow  the  user to
     utilize Diagonal  Scaling and  Incomplete  Cholesky/Incomplete LU
     factorization as preconditioners with no  coding.  The price  for
     this convience is  that one must use  the a specific matrix  data
     structure: SLAP Column or SLAP Triad format.
        Written by Mark K. Seager & Anne Greenbaum

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