A factor 1/2 approximation algorithm for a class of two-stage stochastic mixed-integer programs

Nan Kong; Andrew J. Schaefer

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SPEPS Preprint

Author(s):	Nan Kong, University of Pittsburgh Andrew J. Schaefer, University of Pittsburgh
Title:	A factor 1/2 approximation algorithm for a class of two-stage stochastic mixed-integer programs
Date of Acceptance:	19.02.2004
Submission Date:	13.01.2004
Series Title:	Stochastic Programming E-Print Series (SPEPS)
Editors:	Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint:	pdf (urn:nbn:de:kobv:11-10059449)
Keywords (eng):	Stochastic Programming, Combinatorial Optimization, Approximation Algorithms, Matching
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Abstract (eng):

Abstract We introduce the two-stage stochastic maximum-weight matching problem and demonstrate that this problem is NP-complete. We give a factor 1/2 approximation algorithm and prove its correctness. We also provide a tight example to show the bound given by the algorithm is exactly 1/2 . Computational results on some two-stage stochastic bipartite matching instances indicate that the performance of the approximation algorithm appears to be substantially better than its worst-case performance.

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