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Parallel Dimensionality Reduction for Multiextremal Optimization Problems

  • Victor Gergel
  • Vladimir GrishaginEmail author
  • Ruslan Israfilov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11657)

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.

Keywords

Multiextremal optimization Global optimum Dimensionality reduction Parallel algorithms 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Lobachevsky State UniversityNizhni NovgorodRussia

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