Split-and-merge method for accelerating convergence of stochastic linear programs

Akhil Langer, Udatta Palekar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stochastic program optimizations are computationally very expensive, especially when the number of scenarios are large. Complexity of the focal application, and the slow convergence rate add to its computational complexity. We propose a split-and-merge (SAM) method for accelerating the convergence of stochastic linear programs. SAM splits the original problem into subproblems, and utilizes the dual constraints from the subproblems to accelerate the convergence of the original problem. Our initial results are very encouraging, giving up to 58% reduction in the optimization time. In this paper we discuss the initial results, the ongoing and the future work.

Original languageEnglish (US)
Title of host publicationICORES 2015 - 4th International Conference on Operations Research and Enterprise Systems, Proceedings
EditorsBegona Vitoriano, Greg H. Parlier
PublisherSciTePress
Pages218-223
Number of pages6
ISBN (Electronic)9789897580758
DOIs
StatePublished - 2015
Event4th International Conference on Operations Research and Enterprise Systems, ICORES 2015 - Lisbon, Portugal
Duration: Jan 10 2015Jan 12 2015

Publication series

NameICORES 2015 - 4th International Conference on Operations Research and Enterprise Systems, Proceedings

Other

Other4th International Conference on Operations Research and Enterprise Systems, ICORES 2015
CountryPortugal
CityLisbon
Period1/10/151/12/15

Keywords

  • Decomposition
  • Multicut L-shaped Method
  • Resource Allocation
  • Scenario-based Decomposition
  • Stochastic Optimization
  • US Military Aircraft Allocation

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Control and Systems Engineering

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