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A Population-Based Stochastic Coordinate Descent Method

  • Ana Maria A. C. RochaEmail author
  • M. Fernanda P. Costa
  • Edite M. G. P. Fernandes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

This paper addresses the problem of solving a bound constrained global optimization problem by a population-based stochastic coordinate descent method. To improve efficiency, a small subpopulation of points is randomly selected from the original population, at each iteration. The coordinate descent directions are based on the gradient computed at a special point of the subpopulation. This point could be the best point, the center point or the point with highest score. Preliminary numerical experiments are carried out to compare the performance of the tested variants. Based on the results obtained with the selected problems, we may conclude that the variants based on the point with highest score are more robust and the variants based on the best point less robust, although they win on efficiency but only for the simpler and easy to solve problems.

Keywords

Global optimization Stochastic coordinate descent 

Notes

Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: UID/CEC/00319/2019 and UID/MAT/00013/2013.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ana Maria A. C. Rocha
    • 1
    • 2
    Email author
  • M. Fernanda P. Costa
    • 3
    • 4
  • Edite M. G. P. Fernandes
    • 1
  1. 1.ALGORITMI CenterUniversity of MinhoBragaPortugal
  2. 2.Department of Production and SystemsUniversity of MinhoBragaPortugal
  3. 3.Centre of MathematicsUniversity of MinhoBragaPortugal
  4. 4.Department of MathematicsGuimarãesPortugal

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