A generalized "max-min" sample for surrogate update

Sylvain Lacaze; Samy Missoum

doi:10.1007/s00158-013-1011-9

A generalized "max-min" sample for surrogate update

Sylvain Lacaze, Samy Missoum

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

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 language	English (US)
Pages (from-to)	683-687
Number of pages	5
Journal	Structural and Multidisciplinary Optimization
Volume	49
Issue number	4
DOIs	https://doi.org/10.1007/s00158-013-1011-9
State	Published - Apr 2014

Keywords

Adaptive sampling
Chebychev norm
Max-min sample
Reliability

ASJC Scopus subject areas

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

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10.1007/s00158-013-1011-9

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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.",

keywords = "Adaptive sampling, Chebychev norm, Max-min sample, Reliability",

author = "Sylvain Lacaze and Samy Missoum",

note = "Funding Information: Support from the National Science Foundation (award CMMI-1029257) is gratefully acknowledged.",

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AU - Missoum, Samy

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KW - Max-min sample

KW - Reliability

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A generalized "max-min" sample for surrogate update

Abstract

Keywords

ASJC Scopus subject areas

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