MSPAI

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Modified Sparse Approximate Inverses -- UNDER CONSTRUCTION!!!

Based upon the well-known sparse approximate inverse preconditioner SPAI, we developed the modified sparse approximate inverse (MSPAI) preconditioner.

Theory

MSPAI is a preconditioner for large sparse and ill-conditioned systems of linear equations. We extended the basic SPAI minimization

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to target form and further generalized it in order to add additional probing constraints:

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For an overview of the versatile employment possibilities of our MSPAI formulation, see this pdf taken from here.

Implementation and Features

The entire implementation is done in C++ with parallelization in MPI. Except the Block SPAI approach, we cover the full functionality of SPAI 3.2.

Methodical improvements

  • Extension to target form: allow probing
  • Support for explicit and inverse approximations, both in factorized and unfactorized form and probing of Schur complements, see pdf


Technical improvements

  • Support for complex valued problems
  • Support for sparse QR methods using CSparse by Tim Davis
  • Caching approach to avoid redundant QR decompositions
  • Implementation of QR updates to accelerate pattern update steps
  • Support for maximum sparsity patterns
  • Arbitrary start patterns, i.e. possibility to compute a static SPAI without pattern update steps


Todo

  • Mex interface for MATLAB
  • PetSc interface
  • Support for other LAPACK implementations than ATLAS
  • Wider coverage of file formats for sparse matrices, now support for Matrix Market format only


Download

  • MSPAI 1.1 source code tar ball: []
  • MSPAI manual, closely adapted from SPAI 3.2 manual: []
  • Ph.D. thesis about MSPAI, both theory and implementation: mediatum
  • Details about sparse QR methods in SPAI applications (german only): []
  • Further details about implementation (german only): pdf

MSPAI References

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under construction -- more information about MSPAI implementation and download will be available soon.