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Restarting Strategies for the DQA Algorithm

In: Large Scale Optimization

Author

Listed:
  • Adam J. Berger

    (School of Engineering and Applied Science, Princeton University, Department of Civil Engineering and Operations Research)

  • John M. Mulvey

    (School of Engineering and Applied Science, Princeton University, Department of Civil Engineering and Operations Research)

  • Andrzej Ruszczyński

    (International Institute for Applied Systems Analysis)

Abstract

A scenario-based decomposition algorithm is proposed for large stochastic pro-grams. The subproblem clusters consisting of separable quadratic programs are solved by means of a nonlinear interior point algorithm. Critical implementation issues are analyzed, including restarting and alternative splitting strategies. The approach is suited to a distributed multicomputer such as a network of workstations. Testing with several large LPs (117,000 constraints and 276,000 variables) shows the efficiency of the concepts.

Suggested Citation

  • Adam J. Berger & John M. Mulvey & Andrzej Ruszczyński, 1994. "Restarting Strategies for the DQA Algorithm," Springer Books, in: W. W. Hager & D. W. Hearn & P. M. Pardalos (ed.), Large Scale Optimization, pages 1-25, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-3632-7_1
    DOI: 10.1007/978-1-4613-3632-7_1
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