Assessing Solution Quality in Stochastic Programs

Authors David P. Morton, Guzin Bayraksan



PDF
Thumbnail PDF

File

DagSemProc.05031.6.pdf
  • Filesize: 172 kB
  • 3 pages

Document Identifiers

Author Details

David P. Morton
Guzin Bayraksan

Cite AsGet BibTex

David P. Morton and Guzin Bayraksan. Assessing Solution Quality in Stochastic Programs. In Algorithms for Optimization with Incomplete Information. Dagstuhl Seminar Proceedings, Volume 5031, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.05031.6

Abstract

Assessing whether a solution is of high quality (optimal or near optimal) is a fundamental question in optimization. We develop Monte Carlo sampling-based procedures for assessing solution quality in stochastic programs. Quality is defined via the optimality gap and our procedures' output is a confidence interval on this gap. We review a multiple-replications procedure and then present a result that justifies a computationally simplified single-replication procedure. Even though the single replication procedure is computationally significantly less demanding, the resulting confidence interval may have low coverage for small sample sizes on some problems. We provide variants of this procedure and provide preliminary guidelines for selecting a candidate solution. Both are designed to improve the basic procedure's performance.
Keywords
  • stochastic programming
  • Monte Carlo simulation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail