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Recent Theoretical Advances in Decentralized Distributed Convex Optimization

In: High-Dimensional Optimization and Probability

Author

Listed:
  • Eduard Gorbunov

    (Moscow Institute of Physics and Technology
    Russian Presidential Academy of National Economy and Public Administration)

  • Alexander Rogozin

    (Moscow Institute of Physics and Technology
    Russian Presidential Academy of National Economy and Public Administration)

  • Aleksandr Beznosikov

    (Moscow Institute of Physics and Technology)

  • Darina Dvinskikh

    (Moscow Institute of Physics and Technology
    High School Economic University
    Institute for Information Transmission Problems RAS)

  • Alexander Gasnikov

    (Moscow Institute of Physics and Technology
    Institute for Information Transmission Problems RAS
    Adyghe State University)

Abstract

In the last few years, the theory of decentralized distributed convex optimization has made significant progress. The lower bounds on communications rounds and oracle calls have appeared, as well as methods that reach both of these bounds. In this paper, we focus on how these results can be explained based on optimal algorithms for the non-distributed setup. In particular, we provide our recent results that have not been published yet and that could be found in detail only in arXiv preprints.

Suggested Citation

  • Eduard Gorbunov & Alexander Rogozin & Aleksandr Beznosikov & Darina Dvinskikh & Alexander Gasnikov, 2022. "Recent Theoretical Advances in Decentralized Distributed Convex Optimization," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 253-325, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-00832-0_8
    DOI: 10.1007/978-3-031-00832-0_8
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    Cited by:

    1. Olga Yufereva & Michael Persiianov & Pavel Dvurechensky & Alexander Gasnikov & Dmitry Kovalev, 2024. "Decentralized convex optimization on time-varying networks with application to Wasserstein barycenters," Computational Management Science, Springer, vol. 21(1), pages 1-31, June.
    2. Oleg O. Khamisov & Oleg V. Khamisov & Todor D. Ganchev & Eugene S. Semenkin, 2024. "A Method for Transforming Non-Convex Optimization Problem to Distributed Form," Mathematics, MDPI, vol. 12(17), pages 1-16, September.

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