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A parallel stochastic method for solving linearly constrained concave global minimization problems

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Abstract

A parallel stochastic algorithm is presented for solving the linearly constrained concave global minimization problem. The algorithm is a multistart method and makes use of a Bayesian stopping rule to identify the global minimum with high probability. Computational results are presented for more than 200 problems on a Cray X-MP EA/464 supercomputer.

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Authors and Affiliations

  1. Computer Science Department, United States Naval Academy, 21402, Annapolis, MD, U.S.A.

    A. T. Phillips

  2. Computer Science Department, University of Minnesota, 55455, Minneapolis, MN, U.S.A.

    J. B. Rosen

  3. Tinbergen Institute, Erasmus University, Rotterdam, The Netherlands

    M. Van Vliet

Authors
  1. A. T. Phillips
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  2. J. B. Rosen
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  3. M. Van Vliet
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Phillips, A.T., Rosen, J.B. & Van Vliet, M. A parallel stochastic method for solving linearly constrained concave global minimization problems. J Glob Optim 2, 243–258 (1992). https://doi.org/10.1007/BF00171828

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  • DOI: https://doi.org/10.1007/BF00171828

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