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Asymptotically Optimal Algorithm for the Maximum m-Peripatetic Salesman Problem in a Normed Space

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Learning and Intelligent Optimization (LION 12 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

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Abstract

The maximum m-Peripatetic Salesman Problem (m-PSP) consists of determining m edge-disjoint Hamiltonian cycles of maximum total weight in a given complete weighted n-vertex graph. We consider a geometric variant of the problem and describe a polynomial time approximation algorithm for the m-PSP in a normed space of fixed dimension. We prove that the algorithm is asymptotically optimal for \(m=o(n)\).

Sections 1 and 2 are supported by the RFBR (project 16-07-00168), by the Russian Ministry of Science and Education under the 5-100 Excellence Programme and by the program of fundamental scientific researches of the SB RAS I.5.1. Sections 3 and 4 are supported by Russian Science Foundation (project 16-11-10041)

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Authors and Affiliations

  1. Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia

    E. Kh. Gimadi & O. Yu. Tsidulko

  2. Department of Mechanics and Mathematics, Novosibirsk State University, Novosibirsk, Russia

    E. Kh. Gimadi & O. Yu. Tsidulko

Authors
  1. E. Kh. Gimadi
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  2. O. Yu. Tsidulko
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Correspondence to O. Yu. Tsidulko .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Gimadi, E.K., Tsidulko, O.Y. (2019). Asymptotically Optimal Algorithm for the Maximum m-Peripatetic Salesman Problem in a Normed Space. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_33

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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