变分不等式的近端外推梯度方法。

IF 1.4 3区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Optimization Methods & Software Pub Date : 2017-03-21 eCollection Date: 2018-01-01 DOI:10.1080/10556788.2017.1300899
Yu Malitsky
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引用次数: 53

摘要

本文研究单调变分不等式的一阶新方法。他们使用一种非常简单的线路搜索程序,考虑到操作员的本地信息。此外,该方法不需要算子的Lipschitz连续性,并且直线研究过程仅使用算子的值。此外,当算子是仿射时,我们的直线研究变得非常简单,即只需要简单的向量-向量运算。对于所有的方法,我们都建立了遍历收敛率。此外,我们还对其中一种方法进行了修改,以适应复合最小化的情况。数值实验的初步结果很有希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Proximal extrapolated gradient methods for variational inequalities.

The paper concerns with novel first-order methods for monotone variational inequalities. They use a very simple linesearch procedure that takes into account a local information of the operator. Also, the methods do not require Lipschitz continuity of the operator and the linesearch procedure uses only values of the operator. Moreover, when the operator is affine our linesearch becomes very simple, namely, it needs only simple vector-vector operations. For all our methods, we establish the ergodic convergence rate. In addition, we modify one of the proposed methods for the case of a composite minimization. Preliminary results from numerical experiments are quite promising.

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来源期刊
Optimization Methods & Software
Optimization Methods & Software 工程技术-计算机:软件工程
CiteScore
4.50
自引率
0.00%
发文量
40
审稿时长
7 months
期刊介绍: Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.
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