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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Article number1276932
Journal / PublicationACM Transactions on Modeling and Computer Simulation
Volume17
Issue number4
StatePublished - 1 Sep 2007
Externally publishedYes

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. © 2007 ACM.

Research Area(s)

  • Discrete stochastic optimization, Random search