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Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems

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Book cover Integer Programming and Combinatorial Optimization (IPCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3064))

Abstract

We study the design of approximation algorithms for stochastic combinatorial optimization problems. We formulate the problems in the framework of two-stage stochastic optimization, and provide nearly tight approximations. Our problems range from the simple (shortest path, vertex cover, bin packing) to complex (facility location, set cover), and contain representatives with different approximation ratios.

The approximation ratio of the stochastic variant of a typical problem is of the same order of magnitude as its deterministic counterpart. Furthermore, common techniques for designing approximation algorithms such as LP rounding, the primal-dual method, and the greedy algorithm, can be carefully adapted to obtain these results.

This work was supported in part by NSF grant CCR-0105548 and ITR grant CCR-0122581 (The ALADDIN project).

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Authors and Affiliations

  1. Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA, USA

    R. Ravi & Amitabh Sinha

Authors
  1. R. Ravi
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  2. Amitabh Sinha
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Editors and Affiliations

  1. Department of IEOR, Columbia University, 500 West 120th Street, 10027, New York, NY, USA

    Daniel Bienstock

  2. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    George Nemhauser

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© 2004 Springer-Verlag Berlin Heidelberg

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Ravi, R., Sinha, A. (2004). Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems. In: Bienstock, D., Nemhauser, G. (eds) Integer Programming and Combinatorial Optimization. IPCO 2004. Lecture Notes in Computer Science, vol 3064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25960-2_8

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  • DOI: https://doi.org/10.1007/978-3-540-25960-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22113-5

  • Online ISBN: 978-3-540-25960-2

  • eBook Packages: Springer Book Archive

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