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SPEPS Preprint

Author(s): Lewis Ntaimo, The University of Arizona
Suvrajeet Sen, The University of Arizona
Title: The million-variable "march" for stochastic combinatorial optimization
Date of Acceptance: 19.02.2004
Submission Date: 03.12.2003
Series Title: Stochastic Programming E-Print Series
(SPEPS)
Editors: Julie L. Higle; Werner Römisch; Surrajeet Sen
Keywords (eng): Combinatorial Optimization, Stochastic Mixed Integer Programming, Stochastic Server Location
Appeared in: Journal of global optimization : an international journal dealing with theoretical and computational aspects of seeking global optima and their applications in science, management and engineering 3 (Vol. 32, 2005)
Springer Science + Business Media B.V (Dordrecht [u.a.])
Metadata export: To export the complete metadata set as Endote or Bibtex format please click to the appropriate link. Endnote   Bibtex  

Abstract (eng):
Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result in very large scale combinatorial optimization problems. In this paper, we report on the solution of some of the largest stochastic combinatorial optimization problem consisting of over a million binary variables. While the methodology is quite general, the specific application with which we conduct our experiments arises in stochastic server location problems. The main observation is that stochastic combinatorial optimization problems are comprised of loosely coupled subsystems. By taking advantage of the loosely coupled structure, we show that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.
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