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A Stochastic Method For Global Optimization

Author

Listed:
  • Boender, C. G. E.
  • Rinnooy Kan, A. H. G.
  • Stougie, L.
  • Timmer, G. T.

Abstract

A stochastic method for global optimization is described and evaluated. The method involves a combination of sampling, clustering and local search, and terminates with a range of confidence intervals on the value of the global optimum. Computational results on standard test functions are included as well.

Suggested Citation

  • Boender, C. G. E. & Rinnooy Kan, A. H. G. & Stougie, L. & Timmer, G. T., 1980. "A Stochastic Method For Global Optimization," Econometric Institute Archives 272200, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272200
    DOI: 10.22004/ag.econ.272200
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    References listed on IDEAS

    as
    1. Kester, Adri, 1975. "Asymptotic normality of the number of small distances between random points in a cube," Stochastic Processes and their Applications, Elsevier, vol. 3(1), pages 45-54, January.
    2. van der Hoek, G. & Dijkshoorn, M. W., 1979. "A Numerical Comparison Of Self Scaling Variable Metric Algorithms," Econometric Institute Archives 272178, Erasmus University Rotterdam.
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    Cited by:

    1. Vorst, A. C. F., 1980. "The General Linear Group Of Polynomial Rings Over Regular Rings," Econometric Institute Archives 272201, Erasmus University Rotterdam.
    2. Lenstra, J. K. & Rinnooy Kan, A. H. G., 1980. "A Recursive Approach To The Implementation Of Enumerative Methods," Econometric Institute Archives 272202, Erasmus University Rotterdam.
    3. Telgen, J., 1980. "The Complexity Of The Constrained Gradient Method For Linear Programming," Econometric Institute Archives 272204, Erasmus University Rotterdam.

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