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Asymptotic analysis for penalty and barrier methods in variational inequalities

机译:变分不等式中惩罚和障碍方法的渐近分析

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摘要

We study the convergence of a class of penalty and barrier methods for solving monotone variational inequalities with constraints. This class of methods is an extension of penalty and barrier methods for convex optimization to the setting of variational inequalities. Primal convergence is established under weaker conditions than usual: the solution set is supposed either to be a compact set or, for the case of interior barrier methods, the sum of a compact set and a linear space. Dual convergence is also analyzed. The analysis strongly exploits a new formula related to recession calculus.
机译:我们研究了一类惩罚和障碍方法的收敛性,以解决带约束的单调变分不等式。此类方法是惩罚和障碍方法的扩展,用于凸优化的变分不等式设置。原始收敛是在比平常更弱的条件下建立的:解集被认为是一个紧凑集,或者对于内部障碍方法而言,它是一个紧凑集和一个线性空间的总和。还分析了双重收敛。该分析强烈利用与衰退演算有关的新公式。

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