Kybernetika 45 no. 5, 841-864, 2009

Primal Interior-Point Method for Large Sparse Minimax Optimization

Ladislav Lukšan, Ctirad Matonoha and Jan Vlček


In this paper, we propose a primal interior-point method for large sparse minimax optimization. After a short introduction, the complete algorithm is introduced and important implementation details are given. We prove that this algorithm is globally convergent under standard mild assumptions. Thus the large sparse nonconvex minimax optimization problems can be solved successfully. The results of extensive computational experiments given in this paper confirm efficiency and robustness of the proposed method.


unconstrained optimization, large-scale optimization, minimax optimization, nonsmooth optimization, interior-point methods, modified Newton methods, variable metric methods, computational experiments


9K35, 90C06, 90C47, 90C51