Premium
A modified exchange algorithm for distributional robust optimization and applications in risk management
Author(s) -
Sun Hailin,
Zhang Dali,
Wu SoonYi,
Chen Liang
Publication year - 2022
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12913
Subject(s) - mathematical optimization , computer science , robustness (evolution) , robust optimization , benchmark (surveying) , ambiguity , convex optimization , portfolio , algorithm , portfolio optimization , mathematics , regular polygon , biochemistry , chemistry , geometry , geodesy , financial economics , gene , economics , programming language , geography
Convex semi‐definite semi‐infinite programming problems (SDSIP) represent a special class of distributionally robust optimization (DRO) problems with a wide range of applications in engineering and economics. In this paper, we propose a modified exchange algorithm for convex SDSIP that arises from DRO with matrix moment constraints. We first explore the convergence results of the modified exchange algorithm and perform the efficiency analysis based on a set of benchmark tests. In addition, we apply the SDSIP framework to investigate an optimized certainty bound risk with an ambiguity uncertainty set and implement the algorithm to solve a practical risk minimization problem in portfolio selection. The empirical results show both the efficiency of the algorithm and the robustness of the risk measure.