Quasi-Monte Carlo strategies for stochastic optimization

Shane S. Drew, Tito Homem-de-Mello

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


In this paper we discuss the issue of solving stochastic optimization problems using sampling methods. Numerical results have shown that using variance reduction techniques from statistics can result in significant improvements over Monte Carlo sampling in terms of the number of samples needed for convergence of the optimal objective value and optimal solution to a stochastic optimization problem. Among these techniques are stratified sampling and QuasiMonte Carlo sampling. However, for problems in high dimension, it may be computationally inefficient to calculate Quasi-Monte Carlo point sets in the full dimension. Rather, we wish to identify which dimensions are most important to the convergence and implement a Quasi-Monte Carlo sampling scheme with padding, where the important dimensions are sampled via Quasi-Monte Carlo sampling and the remaining dimensions with Monte Carlo sampling. We then incorporate this sampling scheme into an external sampling algorithm (ES-QMCP) to solve stochastic optimization problems.

Original languageEnglish
Title of host publicationProceedings of the 2006 Winter Simulation Conference, WSC
Number of pages9
StatePublished - 2006
Externally publishedYes
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: 3 Dec 20066 Dec 2006

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2006 Winter Simulation Conference, WSC
Country/TerritoryUnited States
CityMonterey, CA


Dive into the research topics of 'Quasi-Monte Carlo strategies for stochastic optimization'. Together they form a unique fingerprint.

Cite this