A framework for locally convergent random-search algorithms for discrete optimization via simulation

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Original languageEnglish
Article number1276932
Journal / PublicationACM Transactions on Modeling and Computer Simulation
Issue number4
Publication statusPublished - 1 Sep 2007
Externally publishedYes


The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are integer ordered. The framework guarantees desirable asymptotic properties, including almost-sure convergence and known rate of convergence, for any algorithms that conform to its mild conditions. Within this framework, algorithm designers can incorporate sophisticated search schemes and complicated statistical procedures to design new algorithms. © 2007 ACM.

Research Area(s)

  • Discrete stochastic optimization, Random search