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Bayesian Stopping Rules For Multistart Global Optimization Methods

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  • Boender, C. G. E.
  • Rinnooy Kan, A. H. G.

Abstract

By far the most efficient methods for global optimization are based on starting a local optimization routine from an appropriate subset of uniformly distributed starting points. As the number of local optima is frequently unknown in advance, it is a crucial problem when to stop the sequence of sampling and searching. By viewing a set of observed minima as a sample from a generalized multinomial distribution whose cells correspond to the local optima of the objective function, we obtain the posterior distribution of the number of local optima and of the relative size of their regions of attraction. This information is used to construct sequential Bayesian stopping rules which find the optimal trade off between reliability and computational effort.

Suggested Citation

  • 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.
    • Handle: RePEc:ags:eureia:272326
    DOI: 10.22004/ag.econ.272326
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    References listed on IDEAS

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    1. 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.
    2. Rinnooy Kan, A. H. G., 1985. "Probabilistic Analysis Of Algorithms," Econometric Institute Archives 272328, Erasmus University Rotterdam.
    3. Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "Stochastic Global Optimization Methods Part I: Clustering Methods," Econometric Institute Archives 272329, Erasmus University Rotterdam.
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    Cited by:

    1. Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "Stochastic Global Optimization Methods Part I: Clustering Methods," Econometric Institute Archives 272329, Erasmus University Rotterdam.

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