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

Authors:
James E. Whitney, II;Latasha I. Solomon;Stacy D. Hill
Affiliations:
Morgan State University, Baltimore, MD;Morgan State University, Baltimore, MD;Johns Hopkins University, Laurel, MD
Venue:
Proceedings of the 33nd conference on Winter simulation
Year:
2001

Citing 0
Cited 3

Stochastic approximation algorithms for constrained optimization via simulation

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation-based optimization over discrete sets with noisy constraints

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ACM Transactions on Modeling and Computer Simulation (TOMACS)

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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.