In this paper, we propose an interior-point method for large sparse minimax optimization. After a short introduction, where various barrier terms are discussed, the complete algorithm is introduced and some implementation details are given. We prove that this algorithm is globally convergent under standard mild assumptions. Thus nonconvex problems can be solved successfully. The results of computational experiments given in this paper confirm efficiency and robustness of the proposed method.
Citation
Technical Report No. V941, Institute of Computer Science, AV CR, Prague, October 2005.