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A Spectral Partitioning Algorithm for Maximum Directed Cut Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10627))

Abstract

In this paper, we study the maximum directed cut (MaxDC) problem. In the MaxDC, we are given a directed graph with nonnegative edge weights. Our goal is to obtain a bipartition of the vertices such that the total edge weight of the directed cut is maximized. By exploring the combinatorial characteristics of the optimal solution, we offer a 0.272-approximation algorithm based on the technique of spectral partitioning rounding.

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References

  1. Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42, 1115–1145 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  2. Trevisan, L.: Max cut and the smallest eigenvalue. SIAM. Comput. 41, 1769–1786 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  3. Soto, A.: Improved analysis of max-cut algorithm based on spectral partitioning. SIAM J. Diecrete Math. 29, 259–268 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  4. Kale, S., Seshadhri, C.: Combinatorial approximation algorithms for MaxCut using Random Walks. preprint, arXiv:1008.3938 (2010)

  5. Lewin, M., Livnat, D., Zwick, U.: Improved rounding techniques for the MAX 2-SAT and MAX DI-CUT Problems. In: Cook, W.J., Schulz, A.S. (eds.) IPCO 2002. LNCS, vol. 2337, pp. 67–82. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47867-1_6

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  6. Nikiforov, V.: Max k-cut and the smallest eigenvalue. Linear Algebra Appl. 504, 462–467 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  7. Feige, U., Jozeph, S.: Oblivious algorithms for the maximum directed cut problem. Algorithmica 71, 409–428 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  8. Beckenbach, E., Bellman, R.: An Introduction to Inequalities. Random House, New York (1961)

    MATH  Google Scholar 

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Acknowledgments

The first author is supported by Beijing Excellent Talents Funding (No. 2014000020124G046). The second author’s research is supported by Natural Sciences and Engineering Research Council of Canada (NSERC) grant 283106. The third author’s research is supported by NSFC (No. 11501412). The fourth author’s research is supported by NSFC (No. 11531014). The fifth author is supported by Shandong Jianzhu University grant Z0013.

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Correspondence to Dongmei Zhang .

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Zhang, Z., Du, D., Wu, C., Xu, D., Zhang, D. (2017). A Spectral Partitioning Algorithm for Maximum Directed Cut Problem. In: Gao, X., Du, H., Han, M. (eds) Combinatorial Optimization and Applications. COCOA 2017. Lecture Notes in Computer Science(), vol 10627. Springer, Cham. https://doi.org/10.1007/978-3-319-71150-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-71150-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71149-2

  • Online ISBN: 978-3-319-71150-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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