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International Workshop on Approximation Algorithms for Combinatorial Optimization

International Workshop on Randomization and Approximation Techniques in Computer Science

APPROX 2005, RANDOM 2005: Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques pp 14–25Cite as

Rounding Two and Three Dimensional Solutions of the SDP Relaxation of MAX CUT

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3624)

Abstract

Goemans and Williamson obtained an approximation algorithm for the MAX CUT problem with a performance ratio of α GW  ≃ 0.87856. Their algorithm starts by solving a standard SDP relaxation of MAX CUT and then rounds the optimal solution obtained using a random hyperplane. In some cases, the optimal solution of the SDP relaxation happens to lie in a low dimensional space. Can an improved performance ratio be obtained for such instances? We show that the answer is yes in dimensions two and three and conjecture that this is also the case in any higher fixed dimension. In two dimensions an optimal \(\frac{32}{25+5\sqrt{5}}\)-approximation algorithm was already obtained by Goemans. (Note that \(\frac{32}{25+5\sqrt{5}} \simeq 0.88456\).) We obtain an alternative derivation of this result using Gegenbauer polynomials. Our main result is an improved rounding procedure for SDP solutions that lie in ℝ3 with a performance ratio of about 0.8818 . The rounding procedure uses an interesting yin-yan coloring of the three dimensional sphere. The improved performance ratio obtained resolves, in the negative, an open problem posed by Feige and Schechtman [STOC’01]. They asked whether there are MAX CUT instances with integrality ratios arbitrarily close to α GW  ≃ 0.87856 that have optimal embedding, i.e., optimal solutions of their SDP relaxations, that lie in ℝ3.

Keywords

  • Approximation Algorithm
  • Chebyshev Polynomial
  • Performance Ratio
  • Dimensional Solution
  • 45th Annual IEEE Symposium

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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Authors and Affiliations

  1. School of Computer Science, Tel-Aviv University, Tel-Aviv, 69978, Israel

    Adi Avidor & Uri Zwick

Authors
  1. Adi Avidor
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  2. Uri Zwick
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, University of Illinois, 61801, Urbana, IL

    Chandra Chekuri

  2. Institute for Computer Science, University of Kiel, Olshausenstrasse 40, 24118, Kiel, Germany

    Klaus Jansen

  3. Battelle Bâtiment A, Centre Universitaire d’Informatique, Route de Drize 7, 1227, Carouge, Geneva, Switzerland

    José D. P. Rolim

  4. UC Berkeley,  

    Luca Trevisan

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Avidor, A., Zwick, U. (2005). Rounding Two and Three Dimensional Solutions of the SDP Relaxation of MAX CUT. In: Chekuri, C., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds) Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2005 2005. Lecture Notes in Computer Science, vol 3624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538462_2

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  • DOI: https://doi.org/10.1007/11538462_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28239-6

  • Online ISBN: 978-3-540-31874-3

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