A generalized "max-min" sample for surrogate update

Sylvain Lacaze, Samy Missoum

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This brief note describes the generalization of the "max-min" sample that was originally used in the update of approximated feasible or failure domains. The generalization stems from the use of the random variables joint distribution in the sampling scheme. In addition, this note proposes a numerical improvement of the max-min optimization problem through the use of the Chebychev norm.

Original languageEnglish (US)
Pages (from-to)683-687
Number of pages5
JournalStructural and Multidisciplinary Optimization
Volume49
Issue number4
DOIs
StatePublished - 2014

Fingerprint

Min-max
Random variables
Update
Sampling
Min-max Problem
Joint Distribution
Random variable
Optimization Problem
Norm
Generalization

Keywords

  • Adaptive sampling
  • Chebychev norm
  • Max-min sample
  • Reliability

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Control and Systems Engineering
  • Control and Optimization

Cite this

A generalized "max-min" sample for surrogate update. / Lacaze, Sylvain; Missoum, Samy.

In: Structural and Multidisciplinary Optimization, Vol. 49, No. 4, 2014, p. 683-687.

Research output: Contribution to journalArticle

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