{"title":"Delayed Weighted Gradient Method with simultaneous step-sizes for strongly convex optimization","authors":"Hugo Lara, Rafael Aleixo, Harry Oviedo","doi":"10.1007/s10589-024-00586-4","DOIUrl":null,"url":null,"abstract":"<p>The Delayed Weighted Gradient Method (DWGM) is a two-step gradient algorithm that is efficient for the minimization of large scale strictly convex quadratic functions. It has orthogonality properties that make it to compete with the Conjugate Gradient (CG) method. Both methods calculate in sequence two step-sizes, CG minimizes the objective function and DWGM the gradient norm, alongside two search directions defined over first order current and previous iteration information. The objective of this work is to accelerate the recently developed extension of DWGM to nonquadratic strongly convex minimization problems. Our idea is to define the step-sizes of DWGM in a unique two dimensional convex quadratic optimization problem, calculating them simultaneously. Convergence of the resulting algorithm is analyzed. Comparative numerical experiments illustrate the effectiveness of our approach.</p>","PeriodicalId":55227,"journal":{"name":"Computational Optimization and Applications","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Optimization and Applications","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10589-024-00586-4","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
The Delayed Weighted Gradient Method (DWGM) is a two-step gradient algorithm that is efficient for the minimization of large scale strictly convex quadratic functions. It has orthogonality properties that make it to compete with the Conjugate Gradient (CG) method. Both methods calculate in sequence two step-sizes, CG minimizes the objective function and DWGM the gradient norm, alongside two search directions defined over first order current and previous iteration information. The objective of this work is to accelerate the recently developed extension of DWGM to nonquadratic strongly convex minimization problems. Our idea is to define the step-sizes of DWGM in a unique two dimensional convex quadratic optimization problem, calculating them simultaneously. Convergence of the resulting algorithm is analyzed. Comparative numerical experiments illustrate the effectiveness of our approach.
期刊介绍：
Computational Optimization and Applications is a peer reviewed journal that is committed to timely publication of research and tutorial papers on the analysis and development of computational algorithms and modeling technology for optimization. Algorithms either for general classes of optimization problems or for more specific applied problems are of interest. Stochastic algorithms as well as deterministic algorithms will be considered. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome.
Topics of interest include, but are not limited to the following:
Large Scale Optimization,
Unconstrained Optimization,
Linear Programming,
Quadratic Programming Complementarity Problems, and Variational Inequalities,
Constrained Optimization,
Nondifferentiable Optimization,
Integer Programming,
Combinatorial Optimization,
Stochastic Optimization,
Multiobjective Optimization,
Network Optimization,
Complexity Theory,
Approximations and Error Analysis,
Parametric Programming and Sensitivity Analysis,
Parallel Computing, Distributed Computing, and Vector Processing,
Software, Benchmarks, Numerical Experimentation and Comparisons,
Modelling Languages and Systems for Optimization,
Automatic Differentiation,
Applications in Engineering, Finance, Optimal Control, Optimal Design, Operations Research,
Transportation, Economics, Communications, Manufacturing, and Management Science.
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