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Keywords:
unconstrained optimization; large-scale optimization; nonsmooth optimization; generalized minimax optimization; interior-point methods; modified Newton methods; variable metric methods; global convergence; computational experiments
unconstrained optimization; large-scale optimization; nonsmooth optimization; generalized minimax optimization; interior-point methods; modified Newton methods; variable metric methods; global convergence; computational experiments
Summary:
In this paper, we propose a primal interior-point method for large sparse generalized minimax optimization. After a short introduction, where the problem is stated, we introduce the basic equations of the Newton method applied to the KKT conditions and propose a primal interior-point method. (i. e. interior point method that uses explicitly computed approximations of Lagrange multipliers instead of their updates). Next we describe the basic algorithm and give more details concerning its implementation covering numerical differentiation, variable metric updates, and a barrier parameter decrease. Using standard weak assumptions, we prove that this algorithm is globally convergent if a bounded barrier is used. Then, using stronger assumptions, we prove that it is globally convergent also for the logarithmic barrier. Finally, we present results of computational experiments confirming the efficiency of the primal interior point method for special cases of generalized minimax problems.
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