Journal
OPERATIONS RESEARCH LETTERS
Volume 39, Issue 2, Pages 109-114Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.orl.2011.02.007
Keywords
Robust von Neumann minimax theorem; Minimax theorems under payoff uncertainty; Robust optimization; Conjugate functions
Categories
Funding
- Australian Research Council
- Korea government (MEST) [ROA-2008-000-20010-0]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available