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Randomized approximation algorithms in combinatorial optimization

  • Prabhakar Raghavan
Invited Talk
Part of the Lecture Notes in Computer Science book series (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.

Keywords

Approximation Algorithm Vertex Cover Semidefinite Programming Linear Programming Relaxation Multicommodity Flow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Prabhakar Raghavan
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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