Derivative-free robust optimization by outer approximations

We develop an algorithm for minimax problems that arise in robust optimization in the absence of objective function derivatives. The algorithm utilizes an extension of methods for inexact outer approximation in sampling a potentially infinite-cardinality uncertainty set. Clarke stationarity of the a...

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Veröffentlicht in:Mathematical programming Jg. 179; H. 1-2; S. 157 - 193
Hauptverfasser: Menickelly, Matt, Wild, Stefan M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2020
Springer Nature B.V
Springer
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ISSN:0025-5610, 1436-4646
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Zusammenfassung:We develop an algorithm for minimax problems that arise in robust optimization in the absence of objective function derivatives. The algorithm utilizes an extension of methods for inexact outer approximation in sampling a potentially infinite-cardinality uncertainty set. Clarke stationarity of the algorithm output is established alongside desirable features of the model-based trust-region subproblems encountered. We demonstrate the practical benefits of the algorithm on a new class of test problems.
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content type line 14
AC02-06CH11357
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
ISSN:0025-5610
1436-4646
DOI:10.1007/s10107-018-1326-9