Speeding up COMPASS for high-dimensional discrete optimization via simulation

Authors:
L. Jeff Hong;Barry L. Nelson;Jie Xu
Affiliations:
Department of Industrial Engineering and Logistics Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China;Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, United States;Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, United States
Venue:
Operations Research Letters
Year:
2010

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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.