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        SLEPc: Scalable Library for Eigenvalue Problem Computations
        SLEPc: Scalable Library for Eigenvalue Problem Computations
        ===========================================================
        ===========================================================
 
 
                      Developers:
                      Developers:
 
                              Carmen Campos
                              Jose E. Roman
                              Jose E. Roman
                               Eloy Romero
                               Eloy Romero
                              Andres Tomas
                              Andres Tomas
 
 
                      Former collaborators:
                      Former collaborators:
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                              Vicent Vidal
                              Vicent Vidal
 
 
        GRyCAP - Grupo de Redes y Computacion de Altas Prestaciones
        GRyCAP - Grupo de Redes y Computacion de Altas Prestaciones
                Universidad Politecnica de Valencia, Spain
                Universidad Politecnica de Valencia, Spain
 
 
        VERSION: 3.1
        VERSION: 3.2
 
 
 
 
 Overview
 Overview
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 SLEPc, the Scalable Library for Eigenvalue Problem Computations, is a software
 SLEPc, the Scalable Library for Eigenvalue Problem Computations, is a software
 package for  the  solution of large sparse  eigenvalue  problems  on  parallel
 package for  the  solution of large sparse  eigenvalue  problems  on  parallel
 computers. It  can be used for the solution of problems formulated  in  either
 computers. It  can be used for the solution of problems formulated  in  either
 standard  or  generalized form, as well as other related problems such as  the
 standard  or  generalized form, as well as other related problems such as  the
 singular value decomposition.
 singular value decomposition and the quadratic eigenproblem.
 
 
 The  emphasis  of the software is on methods and  techniques  appropriate  for
 The  emphasis  of the software is on methods and  techniques  appropriate  for
 problems  in  which  the  associated matrices are sparse, for  example,  those
 problems  in  which  the  associated matrices are sparse, for  example,  those
 arising after the discretization of partial differential equations. Therefore,
 arising after the discretization of partial differential equations. Therefore,
 most  of  the methods offered by the library are projection methods  or  other
 most  of  the methods offered by the library are projection methods  or  other