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Randomized approximation algorithms in combinatorial optimization

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Foundation of Software Technology and Theoretical Computer Science (FSTTCS 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 880))

Abstract

Randomization has proved to be a powerful technique in finding approximate solutions to difficult problems in combinatorial optimization. In this paper, we restrict ourselves to approximation algorithms that are efficient, and provably good. The focus of this paper is the use of randomized rounding. In this approach, one solves a relaxation of a problem in combinatorial optimization, and then uses randomization to return from the relaxation to the original optimization problem. Two kinds of relaxations of difficult combinatorial problems are considered: linear programming relaxations, and semidefinite programming relaxations. A number of concrete applications are given. This paper does not treat the very interesting applications of randomization in approximation algorithms for counting problems.

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Authors and Affiliations

  1. IBM T.J. Watson Research Center, 10598, Yorktown Heights, NY, USA

    Prabhakar Raghavan

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  1. Prabhakar Raghavan
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P. S. Thiagarajan

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© 1994 Springer-Verlag Berlin Heidelberg

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Raghavan, P. (1994). Randomized approximation algorithms in combinatorial optimization. In: Thiagarajan, P.S. (eds) Foundation of Software Technology and Theoretical Computer Science. FSTTCS 1994. Lecture Notes in Computer Science, vol 880. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58715-2_133

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  • DOI: https://doi.org/10.1007/3-540-58715-2_133

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58715-6

  • Online ISBN: 978-3-540-49054-8

  • eBook Packages: Springer Book Archive

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