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Stochastic Global Optimization Methods Part Ii: Multi Level Methods

Author

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

Abstract

In Part II of this paper, two stochastic methods for global optimization are described that, with probability 1, find all relevant local minima of the objective function with the smallest possible number of local searches. The computational performance of these methods is examined both analytically and empirically.

Suggested Citation

  • 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.
    • Handle: RePEc:ags:eureia:272330
    DOI: 10.22004/ag.econ.272330
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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. "Stochastic Global Optimization Methods Part I: Clustering Methods," Econometric Institute Archives 272329, Erasmus University Rotterdam.
    3. 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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