EURO Journal on Computational Optimization最新文献_第6页

A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty 不确定条件下多资源共享作业调度的混合遗传算法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100050

Hanyu Gu, Hue Chi Lam, Yakov Zinder

引用次数: 0

Celebrating 20 years of EUROpt 庆祝欧盟成立20周年

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100036

Miguel F. Anjos , Tibor Illés , Tamás Terlaky

引用次数: 0

The Marguerite Frank Award for the best EJCO paper 2021 2021年最佳EJCO论文的玛格丽特弗兰克奖

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100026

Immanuel Bomze (Editor-in-Chief)

引用次数: 0

Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations 利用乘法器交替方向法结合层次半可分核近似训练超大规模非线性支持向量机

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100046

S. Cipolla, J. Gondzio

{"title":"Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations","authors":"S. Cipolla, J. Gondzio","doi":"10.1016/j.ejco.2022.100046","DOIUrl":"10.1016/j.ejco.2022.100046","url":null,"abstract":"<div><p>Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher classification quality when compared to linear ones but, at the same time, their computational complexity is prohibitive for large-scale datasets: this drawback is essentially related to the necessity to store and manipulate large, dense and unstructured kernel matrices. Despite the fact that at the core of training an SVM there is a <em>simple</em> convex optimization problem, the presence of kernel matrices is responsible for dramatic performance reduction, making SVMs unworkably slow for large problems. Aiming at an efficient solution of large-scale nonlinear SVM problems, we propose the use of the <em>Alternating Direction Method of Multipliers</em> coupled with <em>Hierarchically Semi-Separable</em> (HSS) kernel approximations. As shown in this work, the detailed analysis of the interaction among their algorithmic components unveils a particularly efficient framework and indeed, the presented experimental results demonstrate, in the case of Radial Basis Kernels, a significant speed-up when compared to the <em>state-of-the-art</em> nonlinear SVM libraries (without significantly affecting the classification accuracy).</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100046"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000223/pdfft?md5=f2bf24cfecce1c28928c1acc1630dd05&pid=1-s2.0-S2192440622000223-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network 机器人舞蹈：在复杂网络中干预新冠肺炎的数学优化平台

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100025

Luis Gustavo Nonato , Pedro Peixoto , Tiago Pereira , Claudia Sagastizábal , Paulo J.S. Silva

{"title":"Robot Dance: A mathematical optimization platform for intervention against COVID-19 in a complex network","authors":"Luis Gustavo Nonato , Pedro Peixoto , Tiago Pereira , Claudia Sagastizábal , Paulo J.S. Silva","doi":"10.1016/j.ejco.2022.100025","DOIUrl":"10.1016/j.ejco.2022.100025","url":null,"abstract":"<div><p>Robot Dance is a computational optimization platform developed in response to the COVID-19 outbreak, to support the decision-making on public policies at a regional level. The tool is suitable for understanding and suggesting levels of intervention needed to contain the spread of infectious diseases when the mobility of inhabitants through a regional network is a concern. Such is the case for the SARS-CoV-2 virus that is highly contagious and, therefore, makes it crucial to incorporate the circulation of people in the epidemiological compartmental models. Robot Dance anticipates the spread of an epidemic in a complex regional network, helping to identify fragile links where applying differentiated measures of containment, testing, and vaccination is important. Based on stochastic optimization, the model determines efficient strategies on the basis of commuting of individuals and the situation of hospitals in each district. Uncertainty in the capacity of intensive care beds is handled by a chance-constraint approach. Some functionalities of Robot Dance are illustrated in the state of São Paulo in Brazil, using real data for a region with more than forty million inhabitants.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100025"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000016/pdfft?md5=481392c4a63aa5d41081f96af794659f&pid=1-s2.0-S2192440622000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46259155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1048

Multi-Neighborhood simulated annealing for the minimum interference frequency assignment problem 最小干扰频率分配问题的多邻域模拟退火

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2021.100024

Sara Ceschia, Luca Di Gaspero, Roberto Maria Rosati, Andrea Schaerf

{"title":"Multi-Neighborhood simulated annealing for the minimum interference frequency assignment problem","authors":"Sara Ceschia, Luca Di Gaspero, Roberto Maria Rosati, Andrea Schaerf","doi":"10.1016/j.ejco.2021.100024","DOIUrl":"https://doi.org/10.1016/j.ejco.2021.100024","url":null,"abstract":"<div><p>We consider the Minimum Interference Frequency Assignment Problem and we propose a novel Simulated Annealing approach that makes use of a portfolio of different neighborhoods, specifically designed for this problem.</p><p>We undertake at once the two versions of the problem proposed by Correia (2001) and by Montemanni et al. (2001), respectively, and the corresponding benchmark instances. With the aim of determining the best configuration of the solver for the specific version of the problem we perform a comprehensive and statistically-principled tuning procedure.</p><p>Even tough a totally precise comparison is not possible, the experimental analysis show that we outperform all previous results on most instances for the first version of the problem, and we are at the same level of the best ones for the second version.</p><p>As a byproduct of this research, we designed a new robust file format for instances and solutions, and a data repository for validating and maintaining the available solutions.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100024"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440621001519/pdfft?md5=bd15ad8aa76d9ff1c05a75649a88a79a&pid=1-s2.0-S2192440621001519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92106812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Newton-MR: Inexact Newton Method with minimum residual sub-problem solver Newton- mr:带最小残差子问题求解的不精确牛顿法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100035

Fred Roosta , Yang Liu , Peng Xu , Michael W. Mahoney

{"title":"Newton-MR: Inexact Newton Method with minimum residual sub-problem solver","authors":"Fred Roosta , Yang Liu , Peng Xu , Michael W. Mahoney","doi":"10.1016/j.ejco.2022.100035","DOIUrl":"10.1016/j.ejco.2022.100035","url":null,"abstract":"<div><p>We consider a variant of inexact Newton Method <span>[20]</span>, <span>[40]</span>, called Newton-MR, in which the least-squares sub-problems are solved approximately using Minimum Residual method <span>[79]</span>. By construction, Newton-MR can be readily applied for unconstrained optimization of a class of non-convex problems known as invex, which subsumes convexity as a sub-class. For invex optimization, instead of the classical Lipschitz continuity assumptions on gradient and Hessian, Newton-MR's global convergence can be guaranteed under a weaker notion of joint regularity of Hessian and gradient. We also obtain Newton-MR's problem-independent local convergence to the set of minima. We show that fast local/global convergence can be guaranteed under a novel inexactness condition, which, to our knowledge, is much weaker than the prior related works. Numerical results demonstrate the performance of Newton-MR as compared with several other Newton-type alternatives on a few machine learning problems.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100035"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000119/pdfft?md5=d469cd05ef15c6b063a51fd431c7a8dd&pid=1-s2.0-S2192440622000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Progress in mathematical programming solvers from 2001 to 2020 2001 - 2020年数学规划解算器的进展

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100031

Thorsten Koch , Timo Berthold , Jaap Pedersen , Charlie Vanaret

引用次数: 18

Upper and lower bounds based on linear programming for the b-coloring problem 基于线性规划的b-着色问题的上下界

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100049

Roberto Montemanni , Xiaochen Chou , Derek H. Smith

{"title":"Upper and lower bounds based on linear programming for the b-coloring problem","authors":"Roberto Montemanni , Xiaochen Chou , Derek H. Smith","doi":"10.1016/j.ejco.2022.100049","DOIUrl":"10.1016/j.ejco.2022.100049","url":null,"abstract":"<div><p>B-coloring is a problem in graph theory. It can model some real applications, as well as being used to enhance solution methods for the classical graph coloring problem. In turn, improved solutions for the classical coloring problem would impact a larger pool of practical applications in several different fields such as scheduling, timetabling and telecommunications. Given a graph <span><math><mi>G</mi><mo>=</mo><mo>(</mo><mi>V</mi><mo>,</mo><mi>E</mi><mo>)</mo></math></span>, the <em>b-coloring problem</em> aims to maximize the number of colors used while assigning a color to every vertex in <em>V</em>, preventing adjacent vertices from receiving the same color, with every color represented by a special vertex, called a b-vertex. A vertex can be a <em>b-vertex</em> only if the set of colors assigned to its adjacent vertices includes all the colors, apart from the one assigned to the vertex itself.</p><p>This work employs methods based on Linear Programming to derive new upper and lower bounds for the problem. In particular, starting from a Mixed Integer Linear Programming model recently presented, upper bounds are obtained through partial linear relaxations of this model, while lower bounds are derived by considering different variations of the original model, modified to target a specific number of colors provided as input. The experimental campaign documented in the paper led to several improvements to the state-of-the-art results.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100049"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000259/pdfft?md5=4554fc69f0108b024eff3393ae695fc6&pid=1-s2.0-S2192440622000259-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128592840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression 具有复杂度保证的非线性共轭梯度法及其在非凸回归中的应用

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100044

Rémi Chan–Renous-Legoubin , Clément W. Royer

{"title":"A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression","authors":"Rémi Chan–Renous-Legoubin , Clément W. Royer","doi":"10.1016/j.ejco.2022.100044","DOIUrl":"10.1016/j.ejco.2022.100044","url":null,"abstract":"<div><p>Nonlinear conjugate gradients are among the most popular techniques for solving continuous optimization problems. Although these schemes have long been studied from a global convergence standpoint, their worst-case complexity properties have yet to be fully understood, especially in the nonconvex setting. In particular, it is unclear whether nonlinear conjugate gradient methods possess better guarantees than first-order methods such as gradient descent. Meanwhile, recent experiments have shown impressive performance of standard nonlinear conjugate gradient techniques on certain nonconvex problems, even when compared with methods endowed with the best known complexity guarantees.</p><p>In this paper, we propose a nonlinear conjugate gradient scheme based on a simple line-search paradigm and a modified restart condition. These two ingredients allow for monitoring the properties of the search directions, which is instrumental in obtaining complexity guarantees. Our complexity results illustrate the possible discrepancy between nonlinear conjugate gradient methods and classical gradient descent. A numerical investigation on nonconvex robust regression problems as well as a standard benchmark illustrate that the restarting condition can track the behavior of a standard implementation.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100044"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S219244062200020X/pdfft?md5=32a8c7d35ac8b53e431514d2573efa79&pid=1-s2.0-S219244062200020X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114696474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7