Derivative-free robust optimization by outer approximations

• 成果类型: Article
• 署名作者: Menickelly, Matt; Wild, Stefan M.
• 署名单位: United States Department of Energy (DOE); Argonne National Laboratory
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-018-1326-9
• 发表日期: 2020
• 页码: 157-193
• 关键词: bundle method; counterparts; algorithm; PROGRAMS

摘要

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.