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Primal interior-point method for large sparse minimax optimization. (English) Zbl 1198.90394
The paper introduces a feasible primal interior point method for solving the problem: minimize $$F(x):=\max_{1\leq i\leq m}f_{i}(x)$$, where $$f_{i}:\mathbb{R}^{m}\rightarrow \mathbb{R}$$ are functions which are bounded below, and have bounded continuous first and second-order derivatives on the convex hull of a level set of $$F$$. It is shown that the algorithm converges globally. In the last section of the paper, the method is compared to three other known methods by testing each one on a set of 22 problems.

##### MSC:
 90C51 Interior-point methods 90C47 Minimax problems in mathematical programming 90C06 Large-scale problems in mathematical programming 49K35 Optimality conditions for minimax problems
##### Software:
KNITRO; SNOPT; UFO; ve08
Full Text:
##### References:
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