一种逼近变分不等式公解和多值映射不动点问题的新迭代投影算法

IF 1.1 Q2 MATHEMATICS, APPLIED
A. O. Owolabi, T. O. Alakoya, A. Taiwo, O. Mewomo
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引用次数: 31

摘要

本文提出了一种改进的自适应惯性次梯度外聚算法,该算法将两个投影投影到半空间上。此外,在温和的条件下,我们得到了在实数Hilbert空间中近似变分不等式问题的公共解和有限半收缩映射族的公共不动点的算法所生成的序列的强收敛性。该算法的主要优点是:在不知道相关单调算子的Lipschitz常数的前提下得到强收敛结果;在半空间上进行两个投影;采用惯性技术加快了收敛速度。最后,我们给出了一个应用和一个数值例子来说明我们的算法的有效性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new inertial-projection algorithm for approximating common solution of variational inequality and fixed point problems of multivalued mappings
In this paper, we present a new modified self-adaptive inertial subgradient extragradient algorithm in which the two projections are made onto some half spaces. Moreover, under mild conditions, we obtain a strong convergence of the sequence generated by our proposed algorithm for approximating a common solution of variational inequality problem and common fixed point of a finite family of demicontractive mappings in a real Hilbert space. The main advantages of our algorithm are: strong convergence result obtained without prior knowledge of the Lipschitz constant of the related monotone operator, the two projections made onto some half-spaces and the inertial technique which speeds up rate of convergence. Finally, we present an application and a numerical example to illustrate the usefulness and applicability of our algorithm.
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来源期刊
CiteScore
3.10
自引率
0.00%
发文量
62
期刊介绍: Numerical Algebra, Control and Optimization (NACO) aims at publishing original papers on any non-trivial interplay between control and optimization, and numerical techniques for their underlying linear and nonlinear algebraic systems. Topics of interest to NACO include the following: original research in theory, algorithms and applications of optimization; numerical methods for linear and nonlinear algebraic systems arising in modelling, control and optimisation; and original theoretical and applied research and development in the control of systems including all facets of control theory and its applications. In the application areas, special interests are on artificial intelligence and data sciences. The journal also welcomes expository submissions on subjects of current relevance to readers of the journal. The publication of papers in NACO is free of charge.
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