Speeding up COMPASS for high-dimensional discrete optimization via simulation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Original languageEnglish
Pages (from-to)550-555
Journal / PublicationOperations Research Letters
Issue number6
Publication statusPublished - Nov 2010
Externally publishedYes


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. © 2010 Elsevier B.V. All rights reserved.

Research Area(s)

  • COMPASS algorithm, Discrete optimization via simulation, Sampling

Citation Format(s)