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

L. Jeff Hong*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number1276932
JournalACM Transactions on Modeling and Computer Simulation
Volume17
Issue number4
DOIs
StatePublished - Sep 1 2007

Keywords

  • Discrete stochastic optimization
  • Random search

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications

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