EURO Journal on Computational Optimization最新文献

A compact model for the home healthcare routing and scheduling problem 家庭医疗保健路由和调度问题的紧凑模型

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2024.100101

Roberto Montemanni , Sara Ceschia , Andrea Schaerf

引用次数: 0

Interior point methods in the year 2025 2025年的内部点法

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100105

Jacek Gondzio

{"title":"Interior point methods in the year 2025","authors":"Jacek Gondzio","doi":"10.1016/j.ejco.2025.100105","DOIUrl":"10.1016/j.ejco.2025.100105","url":null,"abstract":"<div><div>Interior point methods (IPMs) have hugely influenced the field of optimization. Their fast development has been triggered by the seminal paper of Narendra Karmarkar published in 1984 which delivered a polynomial algorithm for linear programming and suggested that it might be implemented into a very efficient method in practice. Indeed, this has been demonstrated within a few years after 1984 and has gained IPMs a status of exceptionally powerful optimization tool. Linear Programming (LP) is at the centre of many operational research techniques including mixed-integer programming, network optimization and various decomposition techniques. Therefore, any progress in LP has far-reaching consequences. IPMs certainly did not disappoint in this context: they have become a heavily used methodology in modern optimization and operational research. Their accuracy, efficiency and reliability have been particularly appreciated when IPMs are applied to truly large scale problems which challenge any alternative approaches.</div><div>In this survey we will discuss several issues related to interior point methods. We will recall techniques which provide the building blocks of IPMs, and observe that actually at least some of them have been developed before 1984. We will briefly comment on the worst-case complexity results for different variants of IPMs and then focus on key aspects of their implementation. We will also address some of the most spectacular features of IPMs and discuss their potential advantages when applied in decomposition algorithms, cutting planes scheme and column generation technique.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100105"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428084","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

Speed planning by minimizing travel time and energy consumption 通过最小化旅行时间和能量消耗来规划速度

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100112

Stefano Ardizzoni, Luca Consolini, Mattia Laurini, Marco Locatelli

引用次数: 0

Efficient use of optimality conditions in Interval Branch and Bound methods 区间分支和定界方法中最优性条件的有效利用

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100108

Mihály Gencsi, Boglárka G.-Tóth

{"title":"Efficient use of optimality conditions in Interval Branch and Bound methods","authors":"Mihály Gencsi, Boglárka G.-Tóth","doi":"10.1016/j.ejco.2025.100108","DOIUrl":"10.1016/j.ejco.2025.100108","url":null,"abstract":"<div><div>The Interval Branch and Bound (IBB) method is a widely used approach for solving nonlinear programming problems where a rigorous solution is required. The method uses Interval Arithmetic (IA) to handle rounding errors in calculations. In the literature, a wide range of variations of IBB exists. However, few IBB implementations use the Karush-Kuhn-Tucker (KKT) or the Fritz-John (FJ) optimality conditions to eliminate non-optimal boxes. The application of the FJ conditions implies to solve a system of interval linear equations, which is often challenging due to overestimation of the boxes. This study focuses on the geometric perspective of the FJ optimality conditions. A preliminary test is introduced, namely the Geometrical Test, which tries to decide when the optimality conditions cannot hold or whether it is convenient to compute the Fritz-John Test. Furthermore, a test case generator is presented that transforms unconstrained problems into constrained test cases by setting a given number of active and inactive constraints at a global optimizer. The efficiency of the Geometrical Test was considered through computational experiments on the generated benchmark. Six variations of the IBB were compared, with or without the FJ condition system and Geometrical Test. The best methods for solving the 272 generated test cases use the designed Geometrical Test with the Lagrange estimator and the Newton step on the normalized interval FJ conditions in most cases.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100108"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068274","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

A diving heuristic for mixed-integer problems with unbounded semi-continuous variables 具有无界半连续变量的混合整数问题的潜水启发式

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100107

Katrin Halbig , Alexander Hoen , Ambros Gleixner , Jakob Witzig , Dieter Weninger

{"title":"A diving heuristic for mixed-integer problems with unbounded semi-continuous variables","authors":"Katrin Halbig , Alexander Hoen , Ambros Gleixner , Jakob Witzig , Dieter Weninger","doi":"10.1016/j.ejco.2025.100107","DOIUrl":"10.1016/j.ejco.2025.100107","url":null,"abstract":"<div><div>Semi-continuous decision variables arise naturally in many real-world applications. They are defined to take either value zero or any value within a specified range, and occur mainly to prevent small nonzero values in the solution. One particular challenge that can come with semi-continuous variables in practical models is that their upper bound may be large or even infinite. In this article, we briefly discuss these challenges, and present a new diving heuristic tailored for mixed-integer optimization problems with general semi-continuous variables. The heuristic is designed to work independently of whether the semi-continuous variables are bounded from above, and thus circumvents the specific difficulties that come with unbounded semi-continuous variables. We conduct extensive computational experiments on three different test sets, integrating the heuristic in an open-source MIP solver. The results indicate that this heuristic is a successful tool for finding high-quality solutions in negligible time. At the root node the primal gap is reduced by an average of 5% up to 21%, and considering the overall performance improvement, the primal integral is reduced by 2% to 17% on average.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100107"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879297","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

Decentralised convex optimisation with probability-proportional-to-size quantization 概率比例量化的分散凸优化

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100113

D.A. Pasechnyuk , P. Dvurechensky , C.A. Uribe , A.V. Gasnikov

引用次数: 0

A tutorial on properties of the epigraph reformulation 关于铭文改写的性质的教程

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100109

Oliver Stein

引用次数: 0

In memoriam: Marguerite Straus Frank (1927–2024) 纪念：玛格丽特·斯特劳斯·弗兰克（1927-2024）

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100104

Immanuel Bomze, Anna Nagurney

引用次数: 0

Direct-search methods in the year 2025: Theoretical guarantees and algorithmic paradigms 2025年的直接搜索方法：理论保证和算法范式

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100110

K.J. Dzahini , F. Rinaldi , C.W. Royer , D. Zeffiro

{"title":"Direct-search methods in the year 2025: Theoretical guarantees and algorithmic paradigms","authors":"K.J. Dzahini , F. Rinaldi , C.W. Royer , D. Zeffiro","doi":"10.1016/j.ejco.2025.100110","DOIUrl":"10.1016/j.ejco.2025.100110","url":null,"abstract":"<div><div>Optimizing a function without using derivatives is a challenging paradigm, that precludes from using classical algorithms from nonlinear optimization, and may thus seem intractable other than by using heuristics. Nevertheless, the field of derivative-free optimization has succeeded in producing algorithms that do not rely on derivatives and yet are endowed with convergence guarantees. One class of such methods, called direct-search methods, is particularly popular thanks to its simplicity of implementation, even though its theoretical underpinnings are not always easy to grasp.</div><div>In this work, we survey contemporary direct-search algorithms from a theoretical viewpoint, with the aim of highlighting the key theoretical features of these methods. We provide a basic introduction to the main classes of direct-search methods, including line-search techniques that have received little attention in earlier surveys. We also put a particular emphasis on probabilistic direct-search techniques and their application to noisy problems, a topic that has undergone significant algorithmic development in recent years. Finally, we complement existing surveys by reviewing the main theoretical advances for solving constrained and multiobjective optimization using direct-search algorithms.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100110"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255125","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

A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification of Alzheimer's disease using handwriting 数据不确定性下同时分类和特征选择的分布式鲁棒机器学习模型：用手写识别阿尔茨海默病的理论、方法和应用

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2025-01-01 DOI: 10.1016/j.ejco.2025.100111

Q.Y. Huang , N.D. Dizon , N. Jeyakumar , V. Jeyakumar

{"title":"A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification of Alzheimer's disease using handwriting","authors":"Q.Y. Huang , N.D. Dizon , N. Jeyakumar , V. Jeyakumar","doi":"10.1016/j.ejco.2025.100111","DOIUrl":"10.1016/j.ejco.2025.100111","url":null,"abstract":"<div><div>In this paper, we introduce an efficient machine learning method based on robust Support Vector Machines (SVMs) that simultaneously classifies data and selects relevant features whilst accounting for data uncertainty. Based on Wasserstein distributionally robust optimization, we develop computationally feasible robust SVM models along with efficient second-order cone programming methods using an integrated application of tools from convex non-smooth analysis and difference-of-convex optimization. Our computational results on benchmark datasets demonstrate that these robust SVMs identify relevant features whilst achieving higher classification accuracies than the conventional (non-robust) SVM models, especially for datasets with more features than instances. Applying our method to a novel dataset of handwriting samples from individuals with Alzheimer's disease and a control group, the model was able to distinguish between both groups with greater than 80% accuracy and using only 37% (168/450) of all available features, outperforming previous SVM models and providing insights into the unique characteristics of the disease.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"13 ","pages":"Article 100111"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680263","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