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Speeding up COMPASS for high-dimensional discrete optimization via simulation

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Speeding up COMPASS for high-dimensional discrete optimization via simulation. / Hong, L. Jeff; Nelson, Barry L.; Xu, Jie.
In: Operations Research Letters, Vol. 38, No. 6, 11.2010, p. 550-555.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hong, LJ, Nelson, BL & Xu, J 2010, 'Speeding up COMPASS for high-dimensional discrete optimization via simulation', Operations Research Letters, vol. 38, no. 6, pp. 550-555. https://doi.org/10.1016/j.orl.2010.09.003

APA

Hong, L. J., Nelson, B. L., & Xu, J. (2010). Speeding up COMPASS for high-dimensional discrete optimization via simulation. Operations Research Letters, 38(6), 550-555. https://doi.org/10.1016/j.orl.2010.09.003

Vancouver

Hong LJ, Nelson BL, Xu J. Speeding up COMPASS for high-dimensional discrete optimization via simulation. Operations Research Letters. 2010 Nov;38(6):550-555. doi: 10.1016/j.orl.2010.09.003

Author

Hong, L. Jeff ; Nelson, Barry L. ; Xu, Jie. / Speeding up COMPASS for high-dimensional discrete optimization via simulation. In: Operations Research Letters. 2010 ; Vol. 38, No. 6. pp. 550-555.

Bibtex

@article{dd28a025b443455c9acf131f8179e9ba,
title = "Speeding up COMPASS for high-dimensional discrete optimization via simulation",
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.",
keywords = "Discrete optimization via simulation, COMPASS algorithm , Sampling",
author = "Hong, {L. Jeff} and Nelson, {Barry L.} and Jie Xu",
year = "2010",
month = nov,
doi = "10.1016/j.orl.2010.09.003",
language = "English",
volume = "38",
pages = "550--555",
journal = "Operations Research Letters",
issn = "0167-6377",
publisher = "Elsevier",
number = "6",

}

RIS

TY - JOUR

T1 - Speeding up COMPASS for high-dimensional discrete optimization via simulation

AU - Hong, L. Jeff

AU - Nelson, Barry L.

AU - Xu, Jie

PY - 2010/11

Y1 - 2010/11

N2 - 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.

AB - 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.

KW - Discrete optimization via simulation

KW - COMPASS algorithm

KW - Sampling

U2 - 10.1016/j.orl.2010.09.003

DO - 10.1016/j.orl.2010.09.003

M3 - Journal article

VL - 38

SP - 550

EP - 555

JO - Operations Research Letters

JF - Operations Research Letters

SN - 0167-6377

IS - 6

ER -