期刊
OPERATIONS RESEARCH LETTERS
卷 39, 期 2, 页码 109-114出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.orl.2011.02.007
关键词
Robust von Neumann minimax theorem; Minimax theorems under payoff uncertainty; Robust optimization; Conjugate functions
资金
- Australian Research Council
- Korea government (MEST) [ROA-2008-000-20010-0]
The celebrated von Neumann minimax theorem is a fundamental theorem in two-person zero-sum games. In this paper, we present a generalization of the von Neumann minimax theorem, called robust von Neumann minimax theorem, in the face of data uncertainty in the payoff matrix via robust optimization approach. We establish that the robust von Neumann minimax theorem is guaranteed for various classes of bounded uncertainties, including the matrix 1-norm uncertainty, the rank-1 uncertainty and the columnwise affine parameter uncertainty. (c) 2011 Elsevier B.V. All rights reserved.
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