Speeding up COMPASS for high-dimensional discrete optimization via simulation

L. Jeff Hong, Barry L. Nelson, Jie Xu

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The convergent optimization via most promising area stochastic search (COMPASS) algorithm is a locally convergent random search algorithm for solving discrete optimization via simulation problems. COMPASS has drawn a significant amount of attention since its introduction. While the asymptotic convergence of COMPASS does not depend on the problem dimension, the finite-time performance of the algorithm often deteriorates as the dimension increases. In this paper, we investigate the reasons for this deterioration and propose a simple change to the solution-sampling scheme that significantly speeds up COMPASS for high-dimensional problems without affecting its convergence guarantee.

Original languageEnglish (US)
Pages (from-to)550-555
Number of pages6
JournalOperations Research Letters
Volume38
Issue number6
DOIs
StatePublished - Nov 1 2010

Keywords

  • COMPASS algorithm
  • Discrete optimization via simulation
  • Sampling

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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