Mathematical Programming

, Volume 22, Issue 1, pp 125–140

A stochastic method for global optimization

  • C. G. E. Boender
  • A. H. G. Rinnooy Kan
  • G. T. Timmer
  • L. Stougie
Article

DOI: 10.1007/BF01581033

Cite this article as:
Boender, C.G.E., Rinnooy Kan, A.H.G., Timmer, G.T. et al. Mathematical Programming (1982) 22: 125. doi:10.1007/BF01581033

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.

Key words

Stochastic MethodsGlobal OptimizationClusteringConfidence Interval

Copyright information

© The Mathematical Programming Society, Inc. 1982

Authors and Affiliations

  • C. G. E. Boender
    • 1
  • A. H. G. Rinnooy Kan
    • 1
  • G. T. Timmer
    • 1
  • L. Stougie
    • 2
  1. 1.Econometric InstituteErasmus UniversityRotterdamThe Netherlands
  2. 2.Mathematical CentreAmsterdamThe Netherlands