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Semidefinite Programming and Approximation Algorithms: A Survey

  • Sanjeev Arora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6139)

Abstract

Computing approximately optimal solutions is an attractive way to cope with NP-hard optimization problems. In the past decade or so, semidefinite programming or SDP (a form of convex optimization that generalizes linear programming) has emerged as a powerful tool for designing such algorithms, and the last few years have seen a profusion of results (worst-case algorithms, average case algorithms, impossibility results, etc).

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sanjeev Arora
    • 1
  1. 1.Computer Science Dept. & Center for Computational IntractabilityPrinceton University 

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