Pub Date : 2024-04-15DOI: 10.1080/10556788.2024.2329588
Frank E. Curtis, Shima Dezfulian, Andreas Wächter
We propose and analyze a model-based derivative-free (DFO) algorithm for solving bound-constrained optimization problems where the objective function is the composition of a smooth function and a v...
我们提出并分析了一种基于模型的无导数（DFO）算法，用于求解目标函数为平滑函数和v...
{"title":"Derivative-free bound-constrained optimization for solving structured problems with surrogate models","authors":"Frank E. Curtis, Shima Dezfulian, Andreas Wächter","doi":"10.1080/10556788.2024.2329588","DOIUrl":"https://doi.org/10.1080/10556788.2024.2329588","url":null,"abstract":"We propose and analyze a model-based derivative-free (DFO) algorithm for solving bound-constrained optimization problems where the objective function is the composition of a smooth function and a v...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1080/10556788.2024.2330635
Tom M. Ragonneau, Zaikun Zhang
This paper demonstrates the optimality of an interpolation set employed in derivative-free trust-region methods. This set is optimal in the sense that it minimizes the constant of well-poisedness i...
{"title":"An optimal interpolation set for model-based derivative-free optimization methods","authors":"Tom M. Ragonneau, Zaikun Zhang","doi":"10.1080/10556788.2024.2330635","DOIUrl":"https://doi.org/10.1080/10556788.2024.2330635","url":null,"abstract":"This paper demonstrates the optimality of an interpolation set employed in derivative-free trust-region methods. This set is optimal in the sense that it minimizes the constant of well-poisedness i...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1080/10556788.2024.2329646
François Pacaud, Michel Schanen, Sungho Shin, Daniel Adrian Maldonado, Mihai Anitescu
We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-...
{"title":"Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures","authors":"François Pacaud, Michel Schanen, Sungho Shin, Daniel Adrian Maldonado, Mihai Anitescu","doi":"10.1080/10556788.2024.2329646","DOIUrl":"https://doi.org/10.1080/10556788.2024.2329646","url":null,"abstract":"We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-07DOI: 10.1080/10556788.2024.2329590
Milan Hladík
This paper introduces the concept of a derivative of the optimal value function in linear programming (LP). Basically, it is the worst case optimal value of an interval LP problem when the nominal ...
{"title":"Linear programming sensitivity measured by the optimal value worst-case analysis","authors":"Milan Hladík","doi":"10.1080/10556788.2024.2329590","DOIUrl":"https://doi.org/10.1080/10556788.2024.2329590","url":null,"abstract":"This paper introduces the concept of a derivative of the optimal value function in linear programming (LP). Basically, it is the worst case optimal value of an interval LP problem when the nominal ...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-25DOI: 10.1080/10556788.2024.2322700
Pham Duy Khanh, Boris S. Mordukhovich, Dat Ba Tran
The paper proposes and develops a novel inexact gradient method (IGD) for minimizing C1-smooth functions with Lipschitzian gradients, i.e. for problems of C1,1 optimization. We show that the sequen...
{"title":"A new inexact gradient descent method with applications to nonsmooth convex optimization","authors":"Pham Duy Khanh, Boris S. Mordukhovich, Dat Ba Tran","doi":"10.1080/10556788.2024.2322700","DOIUrl":"https://doi.org/10.1080/10556788.2024.2322700","url":null,"abstract":"The paper proposes and develops a novel inexact gradient method (IGD) for minimizing C1-smooth functions with Lipschitzian gradients, i.e. for problems of C1,1 optimization. We show that the sequen...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1080/10556788.2024.2320736
Michael Ulbrich, Julia Fritz
We present an analysis of generalized Nash equilibrium problems in infinite-dimensional spaces with possibly non-convex objective functions of the players. Such settings arise, for instance, in gam...
{"title":"On generalized Nash equilibrium problems in infinite-dimensional spaces using Nikaido–Isoda type functionals","authors":"Michael Ulbrich, Julia Fritz","doi":"10.1080/10556788.2024.2320736","DOIUrl":"https://doi.org/10.1080/10556788.2024.2320736","url":null,"abstract":"We present an analysis of generalized Nash equilibrium problems in infinite-dimensional spaces with possibly non-convex objective functions of the players. Such settings arise, for instance, in gam...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1080/10556788.2024.2322095
Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich
Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechani...
{"title":"Decentralized gradient tracking with local steps","authors":"Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich","doi":"10.1080/10556788.2024.2322095","DOIUrl":"https://doi.org/10.1080/10556788.2024.2322095","url":null,"abstract":"Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechani...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140155400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1080/10556788.2024.2320737
Klaus Röbenack, Daniel Gerbet
Most applications of automatic differentiation concern the field of optimization in the broadest sense. This means that many applications only need first and second order derivatives. An exception ...
{"title":"Toward state estimation by high gain differentiators with automatic differentiation","authors":"Klaus Röbenack, Daniel Gerbet","doi":"10.1080/10556788.2024.2320737","DOIUrl":"https://doi.org/10.1080/10556788.2024.2320737","url":null,"abstract":"Most applications of automatic differentiation concern the field of optimization in the broadest sense. This means that many applications only need first and second order derivatives. An exception ...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140155403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.1080/10556788.2024.2318707
T. Giovannelli, G. D. Kent, L. N. Vicente
In this work, we propose different formulations and gradient-based algorithms for deterministic and stochastic bilevel problems with conflicting objectives in the lower level. Such problems have re...
{"title":"Bilevel optimization with a multi-objective lower-level problem: risk-neutral and risk-averse formulations","authors":"T. Giovannelli, G. D. Kent, L. N. Vicente","doi":"10.1080/10556788.2024.2318707","DOIUrl":"https://doi.org/10.1080/10556788.2024.2318707","url":null,"abstract":"In this work, we propose different formulations and gradient-based algorithms for deterministic and stochastic bilevel problems with conflicting objectives in the lower level. Such problems have re...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1080/10556788.2023.2296431
S. Gratton, S. Jerad, Ph. L. Toint
A parametric class of trust-region algorithms for unconstrained non-convex optimization is considered where the value of the objective function is never computed. The class contains a deterministic...
{"title":"Complexity of a class of first-order objective-function-free optimization algorithms","authors":"S. Gratton, S. Jerad, Ph. L. Toint","doi":"10.1080/10556788.2023.2296431","DOIUrl":"https://doi.org/10.1080/10556788.2023.2296431","url":null,"abstract":"A parametric class of trust-region algorithms for unconstrained non-convex optimization is considered where the value of the objective function is never computed. The class contains a deterministic...","PeriodicalId":54673,"journal":{"name":"Optimization Methods & Software","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：