Bayesian approach to global optimization and application to multiobjective and constrained problems

  • J. B. Mockus
  • L. J. Mockus
Contributed Papers

DOI: 10.1007/BF00940509

Cite this article as:
Mockus, J.B. & Mockus, L.J. J Optim Theory Appl (1991) 70: 157. doi:10.1007/BF00940509

Abstract

In this paper, the Bayesian methods of global optimization are considered. They provide the minimal expected deviation from the global minimum. It is shown that, using the Bayesian methods, the asymptotic density of calculations of the objective function is much greater around the point of global minimum. The relation of this density to the parameters of the method and to the function is defined.

Algorithms are described which apply the Bayesian methods to problems with linear and nonlinear constraints. The Bayesian approach to global multiobjective optimization is defined. Interactive procedures and reduction of multidimensional data in the case of global optimization are discussed.

Key Words

Global optimization Bayesian approach multiobjective optimization linear and nonlinear constraints density of observations 

Copyright information

© Plenum Publishing Corporation 1991

Authors and Affiliations

  • J. B. Mockus
    • 1
  • L. J. Mockus
    • 1
  1. 1.Department of Optimal Decision Theory, Institute of Mathematics and CyberneticsAcademy of Sciences of the Lithuanian SSRVilniusLithuania, USSR

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