带伪单调变分不等式的分裂零点问题的惯性方法

IF 1.1 Q2 MATHEMATICS, APPLIED Numerical Algebra Control and Optimization Pub Date : 2021-01-01 DOI:10.3934/naco.2021037
C. C. Okeke, A. Bello, L. Jolaoso, Kingsley Chimuanya Ukandu
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引用次数: 4

摘要

分析了一种新的具有惯性外推步长的外推型算法,用于求解实数Hilbert空间中的自适应分裂零点问题和伪单调变分不等式。此外,本文在不假设关联映射的Lipschitz连续性和算子范数自适应的情况下,得到了较强的收敛结果。此外,该算法在每次迭代中只使用一个在可行集上的投影。在初始因子和迭代参数松弛的条件下,得到了较强的收敛结果。数值结果说明了所提算法的性能。本研究的结果完善和扩展了文献中的相关研究。
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Inertial method for split null point problems with pseudomonotone variational inequality problems
This paper analyzed the new extragradient type algorithm with inertial extrapolation step for solving self adaptive split null point problem and pseudomonotone variational inequality in real Hilbert space. Furthermore, in this study, a strong convergence result is obtained without assuming Lipschitz continuity of the associated mapping and the operator norm is self adaptive. Additionally, the proposed algorithm only uses one projections onto the feasible set in each iteration. More so, the strong convergence results are obtained under some relaxed conditions on the initial factor and the iterative parameters. Numerical results are presented to illustrate the performance of the proposed algorithm.The results obtained in this study improved and extended related studies in the literature.
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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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