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Stochastic Global Optimization Methods Part I: Clustering Methods

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  • Rinnooy Kan, A. H. G.
  • Timmer, G. T.

Abstract

In this stochastic approach to global optimization, clustering techniques are applied to identify local minima of a real valued objective function that are potentially global. Three different methods of this type are described; their accuracy and efficiency are analyzed in detail.

Suggested Citation

  • Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "Stochastic Global Optimization Methods Part I: Clustering Methods," Econometric Institute Archives 272329, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272329
    DOI: 10.22004/ag.econ.272329
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    File URL: https://ageconsearch.umn.edu/record/272329/files/erasmus178.pdf
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    References listed on IDEAS

    as
    1. Francisco J. Solis & Roger J.-B. Wets, 1981. "Minimization by Random Search Techniques," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 19-30, February.
    2. Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "Stochastic Global Optimization Methods Part Ii: Multi Level Methods," Econometric Institute Archives 272330, Erasmus University Rotterdam.
    3. Samuel H. Brooks, 1958. "A Discussion of Random Methods for Seeking Maxima," Operations Research, INFORMS, vol. 6(2), pages 244-251, April.
    4. Boender, C. G. E. & Rinnooy Kan, A. H. G., 1985. "Bayesian Stopping Rules For Multistart Global Optimization Methods," Econometric Institute Archives 272326, Erasmus University Rotterdam.
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    Cited by:

    1. Boender, C. G. E. & Rinnooy Kan, A. H. G., 1985. "Bayesian Stopping Rules For Multistart Global Optimization Methods," Econometric Institute Archives 272326, Erasmus University Rotterdam.
    2. Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "The Multi Level Single Linkage Method For Unconstrained And Constrained Global Optimization," Econometric Institute Archives 272327, Erasmus University Rotterdam.

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