Global non-smooth optimization in robust multivariate regression

Beliakov, Gleb and Kelarev, Andrei 2013, Global non-smooth optimization in robust multivariate regression, Optimization methods and software, vol. 28, no. 1, pp. 124-138.

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Title Global non-smooth optimization in robust multivariate regression
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb
Kelarev, Andrei
Journal name Optimization methods and software
Volume number 28
Issue number 1
Start page 124
End page 138
Total pages 15
Publisher Taylor & Francis
Place of publication Essex, Eng.
Publication date 2013-02
ISSN 1055-6788
Keyword(s) global optimization
high-breakdown regression
least trimmed squares
non-smooth optimization
robust regression
Summary Robust regression in statistics leads to challenging optimization problems. Here, we study one such problem, in which the objective is non-smooth, non-convex and expensive to calculate. We study the numerical performance of several derivative-free optimization algorithms with the aim of computing robust multivariate estimators. Our experiences demonstrate that the existing algorithms often fail to deliver optimal solutions. We introduce three new methods that use Powell's derivative-free algorithm. The proposed methods are reliable and can be used when processing very large data sets containing outliers.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080204 Mathematical Software
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Taylor & Francis
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