{"title":"函数近端映射的各种非扩张性概念相吻合","authors":"Honglin Luo, Xianfu Wang, Xinmin Yang","doi":"10.1137/23m1597009","DOIUrl":null,"url":null,"abstract":"SIAM Journal on Optimization, Volume 34, Issue 1, Page 642-653, March 2024. <br/> Abstract. Proximal mappings are essential in splitting algorithms for both convex and nonconvex optimization. In this paper, we show that proximal mappings of every prox-bounded function are nonexpansive if and only if they are firmly nonexpansive if and only if they are averaged if and only if the function is convex. Lipschitz proximal mappings of prox-bounded functions are also characterized via hypoconvex or strongly convex functions. Our results generalize a recent result due to Rockafellar.","PeriodicalId":49529,"journal":{"name":"SIAM Journal on Optimization","volume":"7 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Various Notions of Nonexpansiveness Coincide for Proximal Mappings of Functions\",\"authors\":\"Honglin Luo, Xianfu Wang, Xinmin Yang\",\"doi\":\"10.1137/23m1597009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SIAM Journal on Optimization, Volume 34, Issue 1, Page 642-653, March 2024. <br/> Abstract. Proximal mappings are essential in splitting algorithms for both convex and nonconvex optimization. In this paper, we show that proximal mappings of every prox-bounded function are nonexpansive if and only if they are firmly nonexpansive if and only if they are averaged if and only if the function is convex. Lipschitz proximal mappings of prox-bounded functions are also characterized via hypoconvex or strongly convex functions. Our results generalize a recent result due to Rockafellar.\",\"PeriodicalId\":49529,\"journal\":{\"name\":\"SIAM Journal on Optimization\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-02-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIAM Journal on Optimization\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1137/23m1597009\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Journal on Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/23m1597009","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
Various Notions of Nonexpansiveness Coincide for Proximal Mappings of Functions
SIAM Journal on Optimization, Volume 34, Issue 1, Page 642-653, March 2024. Abstract. Proximal mappings are essential in splitting algorithms for both convex and nonconvex optimization. In this paper, we show that proximal mappings of every prox-bounded function are nonexpansive if and only if they are firmly nonexpansive if and only if they are averaged if and only if the function is convex. Lipschitz proximal mappings of prox-bounded functions are also characterized via hypoconvex or strongly convex functions. Our results generalize a recent result due to Rockafellar.
期刊介绍:
The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.