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Parallel Implementations of Primal and Dual Algorithms For Matrix Balancing

  • Ismail Chabini
  • Omar Drissi-Kaïtouni
  • Michael Florian
Part of the Advances in Computational Economics book series (AICE, volume 3)

Abstract

We report the parallel computing implementations of a primal projected gradient algorithm and the classical RAS dual algorithm for matrix balancing. The computing platform used is a network of Transputers which is suitable for coarse grained parallelization of sequential algorithms. We report computational results with dense matrices of dimension up to 315 x 315 and 100, 000 nonzero variables.

Keywords

Line Search Parallel Implementation Bipartite Network Dual Algorithm Projected Gradient Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  • Ismail Chabini
  • Omar Drissi-Kaïtouni
  • Michael Florian

There are no affiliations available

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