Exploiting active subspaces in global optimization: How complex is your problem?

Pramudita Satria Palar, Koji Shimoyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

When applying optimization method to a real-world problem, the possession of prior knowledge and preliminary analysis on the landscape of a global optimization problem can give us an insight into the complexity of the problem. This knowledge can better inform us in deciding what optimization method should be used to tackle the problem. However, this analysis becomes problematic when the dimensionality of the problem is high. This paper presents a framework to take a deeper look at the global optimization problem to be tackled: by analyzing the low-dimensional representation of the problem through discovering the active subspaces of the given problem. The virtue of this is that the problem's complexity can be visualized in a one or two-dimensional plot, thus allow one to get a better grip about the problem's diffculty. One could then have a better idea regarding the complexity of their problem to determine the choice of global optimizer or what surrogate-model type to be used. Furthermore, we also demonstrate how the active subspacescan be used to perform design exploration and analysis.

Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1487-1494
Number of pages8
ISBN (Electronic)9781450349390
DOIs
Publication statusPublished - 2017 Jul 15
Event2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany
Duration: 2017 Jul 152017 Jul 19

Publication series

NameGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Other

Other2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
CountryGermany
CityBerlin
Period17/7/1517/7/19

Keywords

  • Active subspace method
  • Complexity analysis
  • Global optimization
  • Low-dimensional representation
  • Surrogate model

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Computer Science Applications

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