Partitioned random search for global optimization with sampling cost and discounting factor

Hengqing Ye, Z. B. Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The method of partitioned random search has been proposed in recent years to obtain an as good as possible solution for the global optimization problem (1). A practical algorithm has been developed and applied to real-life problems. However, the design of this algorithm was based mainly on intuition. The theoretical foundation of the method is an important issue in the development of efficient algorithms for such problems. In this paper, we generalize previous theoretical results and propose a sequential sampling policy for the partitioned random search for global optimization with sampling cost and discounting factor. A proof of the optimality of the proposed sequential sampling policy is given by using the theory of optimal stopping.
Original languageEnglish
Pages (from-to)445-455
Number of pages11
JournalJournal of Optimization Theory and Applications
Volume110
Issue number2
DOIs
Publication statusPublished - 1 Aug 2001
Externally publishedYes

Keywords

  • Dynamic programming
  • Global optimization
  • Partitioned random search
  • Sequential samples

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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