Optimization Methods and Software最新文献_第9页

Optimization Methods and Software Pub Date : 2021-11-02 DOI: 10.1080/10556788.2021.2061218

O. Khamisov, A. Eremeev, Vladimir Ushakov

{"title":"Preface to the special issue","authors":"O. Khamisov, A. Eremeev, Vladimir Ushakov","doi":"10.1080/10556788.2021.2061218","DOIUrl":"https://doi.org/10.1080/10556788.2021.2061218","url":null,"abstract":"This Special Issue of Optimization Methods and Software is devoted to the International Conference ‘Mathematical Optimization Theory and Operations Research’ (MOTOR 2019), held near Ekaterinburg city, Russia, on 8–12 July 2019. The conferences of MOTOR series are descendants of four well-known international and all-Russian conferences, which were held in Ural, Siberia, and the Far East for several decades. The talks presented at these conferences are frequently published in several special issues of well-known journals. To fulfil the standard number of pages per issue, some regular papers were added to this journal issue after the following three papers were originally submitted to the special issue. The paper ‘Cooperative Differential Games with Continuous Updating Using Hamil-ton–Jacobi–Bellman equation‘ by O. Petrosian, A. Tur, Z. Wang, H. Gao investigates cooperative differential games with structures defined for a closed time interval with a fixed duration. The problem of cooperative players’ behaviour is considered. This paper contains a very nice introduction and theoretical results as well as an illustrative example with a model of non-renewable resource extraction. Dynamic Programming for Travelling Salesman with Precedence Constraints: Parallel Morin–Marsten bounding’ implement a parallel Morin–Marsten Branch-and-Bound Scheme for Dynamic Programming and test it on conventional and bottleneck formulations of the Travelling Salesman with Precedence Constraints, using the benchmark instances from TSPLIB. In the experiment, the parallel scheme to scale well with the number of processors and showed significant memory savings, compared to the non-bounded dynamic programming, especially on the large-scale instances. Variational and methods for solving optimization problems, saddle point problems and variational inequalities are proposed. Smooth and nonsmooth cases are considered. This paper provides sufficient theoretical justification and quite comprehensive numerical testing results.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159651","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}

引用次数: 0

An efficient hybrid conjugate gradient method with sufficient descent property for unconstrained optimization 一种有效的混合共轭梯度法，具有充分的下降性质，用于无约束优化

Optimization Methods and Software Pub Date : 2021-10-12 DOI: 10.1080/10556788.2021.1977808

Mina Lotfi, Seyed Mohammad Hosseini

引用次数: 1

Off-line exploration of rectangular cellular environments with a rectangular obstacle 具有矩形障碍物的矩形元胞环境的离线探索

Optimization Methods and Software Pub Date : 2021-10-11 DOI: 10.1080/10556788.2021.1977811

Fatemeh Keshavarz-Kohjerdi

引用次数: 3

Linear systems arising in interior methods for convex optimization: a symmetric formulation with bounded condition number 凸优化的内部方法中的线性系统:一个有界条件数的对称公式

Optimization Methods and Software Pub Date : 2021-10-08 DOI: 10.1080/10556788.2021.1965599

Alexandre Ghannad, D. Orban, Michael A. Saunders

{"title":"Linear systems arising in interior methods for convex optimization: a symmetric formulation with bounded condition number","authors":"Alexandre Ghannad, D. Orban, Michael A. Saunders","doi":"10.1080/10556788.2021.1965599","DOIUrl":"https://doi.org/10.1080/10556788.2021.1965599","url":null,"abstract":"We provide eigenvalues bounds for a new formulation of the step equations in interior methods for convex quadratic optimization. The matrix of our formulation, named , has bounded condition number, converges to a well-defined limit under strict complementarity, and has the same size as the traditional, ill-conditioned, saddle-point formulation. We evaluate the performance in the context of a Matlab object-oriented implementation of PDCO, an interior-point solver for minimizing a smooth convex function subject to linear constraints. The main benefit of our implementation, named PDCOO, is to separate the logic of the interior-point method from the formulation of the system used to compute a step at each iteration and the method used to solve the system. Thus, PDCOO allows easy addition of a new system formulation and/or solution method for experimentation. Our numerical experiments indicate that the formulation has the same storage requirements as the traditional ill-conditioned saddle-point formulation, and its condition is often more favourable than the unsymmetric block formulation.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129405932","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}

引用次数: 2

Exact penalties for decomposable convex optimization problems 可分解凸优化问题的精确惩罚

Optimization Methods and Software Pub Date : 2021-09-28 DOI: 10.1080/10556788.2021.1977807

I. Konnov

引用次数: 0

Outer approximation algorithms for convex vector optimization problems 凸向量优化问题的外逼近算法

Optimization Methods and Software Pub Date : 2021-09-15 DOI: 10.1080/10556788.2023.2167994

Irem Nur Keskin, Firdevs Ulus

引用次数: 5

Study on precoding optimization algorithms in massive MIMO system with multi-antenna users 多天线用户大规模MIMO系统预编码优化算法研究

Optimization Methods and Software Pub Date : 2021-07-28 DOI: 10.1080/10556788.2022.2091564

E. Bobrov, D. Kropotov, S. Troshin, D. Zaev

引用次数: 4

Full-low evaluation methods for derivative-free optimization 无导数优化的全低评价方法

Optimization Methods and Software Pub Date : 2021-07-25 DOI: 10.1080/10556788.2022.2142582

A. Berahas, Oumaima Sohab, L. N. Vicente

{"title":"Full-low evaluation methods for derivative-free optimization","authors":"A. Berahas, Oumaima Sohab, L. N. Vicente","doi":"10.1080/10556788.2022.2142582","DOIUrl":"https://doi.org/10.1080/10556788.2022.2142582","url":null,"abstract":"We propose a new class of rigorous methods for derivative-free optimization with the aim of delivering efficient and robust numerical performance for functions of all types, from smooth to non-smooth, and under different noise regimes. To this end, we have developed a class of methods, called Full-Low Evaluation methods, organized around two main types of iterations. The first iteration type (called Full-Eval) is expensive in function evaluations, but exhibits good performance in the smooth and non-noisy cases. For the theory, we consider a line search based on an approximate gradient, backtracking until a sufficient decrease condition is satisfied. In practice, the gradient was approximated via finite differences, and the direction was calculated by a quasi-Newton step (BFGS). The second iteration type (called Low-Eval) is cheap in function evaluations, yet more robust in the presence of noise or non-smoothness. For the theory, we consider direct search, and in practice we use probabilistic direct search with one random direction and its negative. A switch condition from Full-Eval to Low-Eval iterations was developed based on the values of the line-search and direct-search stepsizes. If enough Full-Eval steps are taken, we derive a complexity result of gradient-descent type. Under failure of Full-Eval, the Low-Eval iterations become the drivers of convergence yielding non-smooth convergence. Full-Low Evaluation methods are shown to be efficient and robust in practice across problems with different levels of smoothness and noise.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322005","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}

引用次数: 3

Quadratic rates of asymptotic regularity for the Tikhonov–Mann iteration Tikhonov-Mann迭代的渐近正则性的二次率

Optimization Methods and Software Pub Date : 2021-07-15 DOI: 10.1080/10556788.2022.2060974

Horaţiu Cheval, Laurentiu Leustean

引用次数: 8

A new multipoint symmetric secant method with a dense initial matrix 一种具有密集初始矩阵的多点对称割线新方法

Optimization Methods and Software Pub Date : 2021-07-13 DOI: 10.1080/10556788.2023.2167993

Jennifer B. Erway, Mostafa Rezapour

引用次数: 7