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Mix-Matrix Transformation Method for Max-Сut Problem

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Artificial Neural Networks and Machine Learning – ICANN 2014 (ICANN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

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Abstract

One usually tries to raise the efficiency of optimization techniques by changing the dynamics of local optimization. In contrast to the above approach, we propose changing the surface of the problem rather than the dynamics of local search. The Mix-Matrix algorithm proposed by the authors previously [1] realizes such transformation and can be applied directly to a max-cut problem and successfully compete with other popular algorithms in this field such as CirCut and Scatter Search.

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Authors and Affiliations

  1. Scientific Research Institute for System Analysis of Russian Academy of Sciences, Vavilova str. 44b.2, 119333, Moscow, Russia

    Iakov Karandashev & Boris Kryzhanovsky

Authors
  1. Iakov Karandashev
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  2. Boris Kryzhanovsky
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter , Cornelius Weber  & Sven Magg ,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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Karandashev, I., Kryzhanovsky, B. (2014). Mix-Matrix Transformation Method for Max-Сut Problem. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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