{"title":"Modified general splitting method for the split feasibility problem","authors":"Seakweng Vong, Zhongsheng Yao","doi":"10.1007/s10898-024-01399-9","DOIUrl":null,"url":null,"abstract":"<p>Based on the equivalent optimization problems of the splitting feasibility problem, we investigate this problem by using modified general splitting method in this paper. One is a relaxation splitting method with linearization, and the other combines the former with alternated inertial extrapolation step. The strong convergence of our algorithms is analyzed when related parameters are properly chosen. Compared with most existing results where inertial factor must be less than 1, inertial factor can be taken 1 in our alternated inertial-type algorithm. The efficiency of our methods are illustrated by some numerical examples.\n</p>","PeriodicalId":15961,"journal":{"name":"Journal of Global Optimization","volume":"16 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Global Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10898-024-01399-9","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
Abstract
Based on the equivalent optimization problems of the splitting feasibility problem, we investigate this problem by using modified general splitting method in this paper. One is a relaxation splitting method with linearization, and the other combines the former with alternated inertial extrapolation step. The strong convergence of our algorithms is analyzed when related parameters are properly chosen. Compared with most existing results where inertial factor must be less than 1, inertial factor can be taken 1 in our alternated inertial-type algorithm. The efficiency of our methods are illustrated by some numerical examples.
期刊介绍：
The Journal of Global Optimization publishes carefully refereed papers that encompass theoretical, computational, and applied aspects of global optimization. While the focus is on original research contributions dealing with the search for global optima of non-convex, multi-extremal problems, the journal’s scope covers optimization in the widest sense, including nonlinear, mixed integer, combinatorial, stochastic, robust, multi-objective optimization, computational geometry, and equilibrium problems. Relevant works on data-driven methods and optimization-based data mining are of special interest.
In addition to papers covering theory and algorithms of global optimization, the journal publishes significant papers on numerical experiments, new testbeds, and applications in engineering, management, and the sciences. Applications of particular interest include healthcare, computational biochemistry, energy systems, telecommunications, and finance. Apart from full-length articles, the journal features short communications on both open and solved global optimization problems. It also offers reviews of relevant books and publishes special issues.
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