Split-and-Merge Method for Accelerating Convergence of Stochastic Linear Programs

Akhil Langer, Udatta Palekar

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.

References

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Paper Citation


in Harvard Style

Langer A. and Palekar U. (2015). Split-and-Merge Method for Accelerating Convergence of Stochastic Linear Programs . In Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-075-8, pages 218-223. DOI: 10.5220/0005287902180223


in Bibtex Style

@conference{icores15,
author={Akhil Langer and Udatta Palekar},
title={Split-and-Merge Method for Accelerating Convergence of Stochastic Linear Programs},
booktitle={Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2015},
pages={218-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005287902180223},
isbn={978-989-758-075-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Split-and-Merge Method for Accelerating Convergence of Stochastic Linear Programs
SN - 978-989-758-075-8
AU - Langer A.
AU - Palekar U.
PY - 2015
SP - 218
EP - 223
DO - 10.5220/0005287902180223