Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa
{"title":"Tight analyses for subgradient descent I: Lower bounds","authors":"Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa","doi":"10.5802/ojmo.31","DOIUrl":"https://doi.org/10.5802/ojmo.31","url":null,"abstract":"Consider the problem of minimizing functions that are Lipschitz and convex, but not necessarily differentiable. We construct a function from this class for which the T th iterate of subgradient descent has error Ω(log( T ) / √ T ) . This matches a known upper bound of O (log( T ) / √ T ) . We prove analogous results for functions that are additionally strongly convex. There exists such a function for which the error of the T th iterate of subgradient descent has error Ω(log( T ) /T ) , matching a known upper bound of O (log( T ) /T ) . These results resolve a question posed by Shamir (2012)","PeriodicalId":477184,"journal":{"name":"Open journal of mathematical optimization","volume":"119 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The continuous quadrant penalty formulation of logical constraints","authors":"Sonia Cafieri, Andrew Conn, Marcel Mongeau","doi":"10.5802/ojmo.28","DOIUrl":"https://doi.org/10.5802/ojmo.28","url":null,"abstract":"Could continuous optimization address efficiently logical constraints? We propose a continuous-optimization alternative to the usual discrete-optimization (big-M and complementary) formulations of logical constraints, that can lead to effective practical methods. Based on the simple idea of guiding the search of a continuous-optimization descent method towards the parts of the domain where the logical constraint is satisfied, we introduce a smooth penalty-function formulation of logical constraints, and related theoretical results. This formulation allows a direct use of state-of-the-art continuous optimization solvers. The effectiveness of the continuous quadrant penalty formulation is demonstrated on an aircraft conflict avoidance application.","PeriodicalId":477184,"journal":{"name":"Open journal of mathematical optimization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}