Lennin Mallma Ramirez , Nelson Maculan , Adilson Elias Xavier , Vinicius Layter Xavier
{"title":"错位双曲增强拉格朗日算法的约束条件","authors":"Lennin Mallma Ramirez , Nelson Maculan , Adilson Elias Xavier , Vinicius Layter Xavier","doi":"10.1016/j.rico.2024.100429","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we study an augmented Lagrangian-type algorithm called the Dislocation Hyperbolic Augmented Lagrangian Algorithm (DHALA), which solves an inequality nonconvex optimization problem. We show that the sequence generated by DHALA converges to a Karush–Kuhn–Tucker (KKT) point under the Mangasarian–Fromovitz constraint qualification. The contribution of our work is to consider a constraint qualification into this algorithm. Finally, we present some computational illustrations to demonstrate the performance our algorithm works.</p></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"15 ","pages":"Article 100429"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666720724000596/pdfft?md5=6b2765e03f40da9b2d8677e849370378&pid=1-s2.0-S2666720724000596-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A constraint qualification for the dislocation hyperbolic augmented Lagrangian algorithm\",\"authors\":\"Lennin Mallma Ramirez , Nelson Maculan , Adilson Elias Xavier , Vinicius Layter Xavier\",\"doi\":\"10.1016/j.rico.2024.100429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we study an augmented Lagrangian-type algorithm called the Dislocation Hyperbolic Augmented Lagrangian Algorithm (DHALA), which solves an inequality nonconvex optimization problem. We show that the sequence generated by DHALA converges to a Karush–Kuhn–Tucker (KKT) point under the Mangasarian–Fromovitz constraint qualification. The contribution of our work is to consider a constraint qualification into this algorithm. Finally, we present some computational illustrations to demonstrate the performance our algorithm works.</p></div>\",\"PeriodicalId\":34733,\"journal\":{\"name\":\"Results in Control and Optimization\",\"volume\":\"15 \",\"pages\":\"Article 100429\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666720724000596/pdfft?md5=6b2765e03f40da9b2d8677e849370378&pid=1-s2.0-S2666720724000596-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Results in Control and Optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666720724000596\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Control and Optimization","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666720724000596","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
A constraint qualification for the dislocation hyperbolic augmented Lagrangian algorithm
In this paper, we study an augmented Lagrangian-type algorithm called the Dislocation Hyperbolic Augmented Lagrangian Algorithm (DHALA), which solves an inequality nonconvex optimization problem. We show that the sequence generated by DHALA converges to a Karush–Kuhn–Tucker (KKT) point under the Mangasarian–Fromovitz constraint qualification. The contribution of our work is to consider a constraint qualification into this algorithm. Finally, we present some computational illustrations to demonstrate the performance our algorithm works.