针对变分不等式问题的可行不精确投影外梯度法

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
R. Díaz Millán, O. P. Ferreira, J. Ugon
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引用次数: 0

摘要

本文探讨了有限维欧几里得空间中的变分不等式问题,并提出了两种外梯度法的非精确变体来解决这一问题。所提出的方法不像以前的外梯度法那样计算约束集上的精确投影,而是利用相对误差准则计算约束集上可行的非精确投影。所提出方法的第一个版本与经典形式的外梯度法相对应,具有恒定步长。为了确定其收敛性,我们需要假设算子是伪单调和 Lipschitz 连续的,就像标准方法一样。对于第二个版本,所介绍的方法不是固定步长,而是通过线性搜索在每次迭代中找到合适的步长。与经典的外梯度法一样,所提出的方法在每次迭代中只对可行集进行两次投影。在不对定义变分不等式问题的算子进行 Lipschitz 连续性假设的情况下,提供了完整的收敛性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Extragradient method with feasible inexact projection to variational inequality problem

Extragradient method with feasible inexact projection to variational inequality problem

The variational inequality problem in finite-dimensional Euclidean space is addressed in this paper, and two inexact variants of the extragradient method are proposed to solve it. Instead of computing exact projections on the constraint set, as in previous versions extragradient method, the proposed methods compute feasible inexact projections on the constraint set using a relative error criterion. The first version of the proposed method provided is a counterpart to the classic form of the extragradient method with constant steps. In order to establish its convergence we need to assume that the operator is pseudo-monotone and Lipschitz continuous, as in the standard approach. For the second version, instead of a fixed step size, the method presented finds a suitable step size in each iteration by performing a line search. Like the classical extragradient method, the proposed method does just two projections into the feasible set in each iteration. A full convergence analysis is provided, with no Lipschitz continuity assumption of the operator defining the variational inequality problem.

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来源期刊
CiteScore
3.70
自引率
9.10%
发文量
91
审稿时长
10 months
期刊介绍: Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. Topics of interest include, but are not limited to the following: Large Scale Optimization, Unconstrained Optimization, Linear Programming, Quadratic Programming Complementarity Problems, and Variational Inequalities, Constrained Optimization, Nondifferentiable Optimization, Integer Programming, Combinatorial Optimization, Stochastic Optimization, Multiobjective Optimization, Network Optimization, Complexity Theory, Approximations and Error Analysis, Parametric Programming and Sensitivity Analysis, Parallel Computing, Distributed Computing, and Vector Processing, Software, Benchmarks, Numerical Experimentation and Comparisons, Modelling Languages and Systems for Optimization, Automatic Differentiation, Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research, Transportation, Economics, Communications, Manufacturing, and Management Science.
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