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Title: Derivative-free robust optimization by outer approximations

Journal Article · · Mathematical Programming
ORCiD logo [1];  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
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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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1631967
Journal Information:
Mathematical Programming, Journal Name: Mathematical Programming Journal Issue: 1-2 Vol. 179; ISSN 0025-5610
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

A survey of robust optimization based machine learning with special reference to support vector machines journal December 2019

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Derivative-free optimization
Manifold sampling
Outer approximation algorithms
Robust optimization