Constrained optimization over discrete sets via SPSA with application to non-separable resource allocation

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Constrained optimization over discrete sets via SPSA with application to non-separable resource allocation

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Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents

Arlington, Virginia

SESSION: Analysis methodology table of contents

Pages: 313 - 317

Year of Publication: 2001

ISBN:0-7803-7309-X

Authors

James E. Whitney, II	Morgan State University, Baltimore, MD
Latasha I. Solomon	Morgan State University, Baltimore, MD
Stacy D. Hill	Johns Hopkins University, Laurel, MD

Sponsors

INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association

Publisher

IEEE Computer Society Washington, DC, USA

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ABSTRACT

This paper presents a version of the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm for optimizing non-separable functions over discrete sets under given constraints. The primary motivation for discrete SPSA is to solve a class of resource allocation problems wherein the goal is to distribute a finite number of discrete resources to finitely many users in such a way as to optimize a specified objective function. The basic algorithm and the application of the algorithm to the optimal resource allocation problem is discussed and simulation results are presented which illustrate its performance.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Einbu, J. M. "A finite method for the solution of a multi-resource allocation problem with concave return functions", Mathematics of Operations Research, Vol. 9, No.2, pp. 232-243, May 1984.
	2	Gerencsér, L., Hill, S. D. and Vágó, Z.. "Optimization over discrete sets via SPSA," Proceedings of the Conference on Decision and Control, CDC 38, pp. 1791-1794, 1999.
	3	Spall, J. C. "Multivariate stochastic approximation using a simultaneous perturbation gradient approximation," IEEE Transactions on Automatic Control, vol. 37, pp. 332-341, 1992.
	4	Wang, I-J. and Spall, J. C. "A constrained simultaneous perturbation stochastic approximation algorithm based on penalty functions", Proceedings of the American Control Conference, pp. 393-399, San Diego, California, June 1999.
	5	Whitney, J. and Hill, S. D. "Constrained optimization over discrete sets via spsa with application to resource allocation", Proceeding of the 35th Annual Conference on Information Sciences and Systems, pp. 515-518, March 2001.