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Parallel Dimensionality Reduction for Multiextremal Optimization Problems

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Parallel Computing Technologies (PaCT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11657))

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Abstract

The paper is devoted to consideration of numerical global optimization methods in the framework of the approach of reducing dimensionality based on nested optimization schemes. For the adaptive nested scheme being more efficient in comparison with its classical prototype a new algorithm of parallel implementation is proposed. General descriptions of the parallel techniques both for synchronous and asynchronous versions are given. Results of numerical experiments on a set of complicated multiextremal test problems of high dimension are presented. These results demonstrate essential acceleration of asynchronous parallel algorithm in comparison with the sequential version.

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Acknowledgements

The research has been supported by the Russian Science Foundation, project No 16-11-10150 “Novel efficient methods and software tools for time-consuming decision make problems using superior-performance supercomputers”.

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Authors and Affiliations

  1. Lobachevsky State University, Gagarin Avenue 23, 603950, Nizhni Novgorod, Russia

    Victor Gergel, Vladimir Grishagin & Ruslan Israfilov

Authors
  1. Victor Gergel
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  2. Vladimir Grishagin
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  3. Ruslan Israfilov
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Correspondence to Vladimir Grishagin .

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Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Malyshkin

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Gergel, V., Grishagin, V., Israfilov, R. (2019). Parallel Dimensionality Reduction for Multiextremal Optimization Problems. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_13

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  • DOI: https://doi.org/10.1007/978-3-030-25636-4_13

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