EURO Journal on Computational Optimization最新文献

{"title":"Optimization Challenges in Data Science – Special Issue Editorial","authors":"Coralia Cartis , Panayotis Mertikopoulos","doi":"10.1016/j.ejco.2023.100064","DOIUrl":"10.1016/j.ejco.2023.100064","url":null,"abstract":"","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100064"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46448319","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}

{"title":"Laplacian-based semi-Supervised learning in multilayer hypergraphs by coordinate descent","authors":"Sara Venturini ,&nbsp;Andrea Cristofari ,&nbsp;Francesco Rinaldi ,&nbsp;Francesco Tudisco","doi":"10.1016/j.ejco.2023.100079","DOIUrl":"https://doi.org/10.1016/j.ejco.2023.100079","url":null,"abstract":"<div><p>Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100079"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440623000230/pdfft?md5=6f643343ec1f2c6f80d72a373b75a084&pid=1-s2.0-S2192440623000230-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91964081","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}

{"title":"Branch-and-cut solution approach for multilevel mixed integer linear programming problems","authors":"Ashenafi Awraris ,&nbsp;Berhanu Guta Wordofa ,&nbsp;Semu Mitiku Kassa","doi":"10.1016/j.ejco.2023.100076","DOIUrl":"https://doi.org/10.1016/j.ejco.2023.100076","url":null,"abstract":"<div><p>A multilevel programming problem is an optimization problem that involves multiple decision makers, whose decisions are made in a sequential (or hierarchical) order. If all objective functions and constraints are linear and some decision variables in any level are restricted to take on integral or discrete values, then the problem is called a multilevel mixed integer linear programming problem (ML-MILP). Such problems are known to have disconnected feasible regions (called inducible regions), making the task of constructing an optimal solution challenging. Therefore, existing solution approaches are limited to some strict assumptions in the model formulations and lack universality. This paper presents a branch-and-cut (B&amp;C) algorithm for the global solution of such problems with any finite number of hierarchical levels, and containing both continuous and discrete variables at each level of the decision-making hierarchy. Finite convergence of the proposed algorithm to a global solution is established. Numerical examples are used to illustrate the detailed procedure and to demonstrate the performance of the algorithm. Additionally, the computational performance of the proposed method is studied by comparing it with existing method through some selected numerical examples.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100076"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49742854","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}

{"title":"A parameterized lower bounding method for the open capacitated arc routing problem","authors":"Rafael Kendy Arakaki,&nbsp;Fábio Luiz Usberti","doi":"10.1016/j.ejco.2023.100080","DOIUrl":"https://doi.org/10.1016/j.ejco.2023.100080","url":null,"abstract":"<div><p>Consider an undirected graph with demands scattered over the edges and a homogeneous fleet of vehicles to service the demands. In the <em>open capacitated arc routing problem</em> (OCARP) the objective is to find a set of routes that collectively service all demands with the minimum cost. Each vehicle has limited capacity and it can start and finish the route at any node. The OCARP is NP-hard, and its applications include meter reading and cutting path determination problems. State-of-the-art solution methods developed for the OCARP are heuristics, which show good tradeoffs between solution quality and processing time, but do not provide optimality certificates of the obtained solutions. This work focuses on a lower bounding method for the OCARP which can be used to better assess the quality of heuristic solutions. We propose the Relaxed Flow method (<span><math><mi>R</mi><mi>F</mi><mo>(</mo><mi>k</mi><mo>)</mo></math></span>) which involves the resolution of a mixed integer linear formulation where all vehicles' capacities are modeled as flows on an augmented graph. A parameter <em>k</em> controls the model tightness and <span><math><mi>R</mi><mi>F</mi><mo>(</mo><mi>k</mi><mo>)</mo></math></span> is shown to be at least as tight as the well-known Belenguer and Benavent's formulation for any <span><math><mi>k</mi><mo>⩾</mo><mn>0</mn></math></span>. To strengthen the model, capacity cuts were included in <span><math><mi>R</mi><mi>F</mi><mo>(</mo><mi>k</mi><mo>)</mo></math></span> by means of a branch-and-cut framework. Extensive computational experiments conducted on a set of benchmark instances revealed that our method outperformed previous methods. Computational experiments also demonstrated the importance of the parameterization technique to obtain good results. The previously known lower bounds were improved substantially and optimality certificates were attained in 78.9% of the instances. As far as we know this is the first parameterized lower bounding method proposed for an arc routing problem, and we argue it can be generalized to other variants of arc routing problems and general routing problems.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100080"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440623000242/pdfft?md5=47d32c2d05c48fe7d4f22b5727ef4bc9&pid=1-s2.0-S2192440623000242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92025778","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}

{"title":"Maximum-margin polyhedral separation for binary Multiple Instance Learning","authors":"Annabella Astorino ,&nbsp;Matteo Avolio ,&nbsp;Antonio Fuduli","doi":"10.1016/j.ejco.2023.100070","DOIUrl":"10.1016/j.ejco.2023.100070","url":null,"abstract":"<div><p>Multiple Instance Learning (MIL) is a kind of weak supervised learning, where each sample is represented by a bag of instances. The main characteristic of such problems resides in the training phase, since the class labels are provided only for each bag, whereas the instance labels are unknown.</p><p>We focus on binary MIL problems characterized by two types of instances (positive and negative): based on the standard MIL assumption, a bag is considered positive if at least one of its instances is positive and it is considered negative otherwise. Then our idea is to generate a maximum-margin polyhedral separation surface such that, for each positive bag, at least one of its instances is inside the polyhedron and all the instances of the negative bags are outside. The resulting optimization problem is a nonlinear, nonconvex and nonsmooth mixed integer program, that we heuristically solve by a Block Coordinate Descent type method, based on repeatedly applying the DC (Difference of Convex) Algorithm.</p><p>Numerical results are presented on a set of benchmark datasets.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100070"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42034951","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}

{"title":"Hierarchical distributed optimization of constraint-coupled convex and mixed-integer programs using approximations of the dual function","authors":"Vassilios Yfantis ,&nbsp;Simon Wenzel ,&nbsp;Achim Wagner ,&nbsp;Martin Ruskowski ,&nbsp;Sebastian Engell","doi":"10.1016/j.ejco.2023.100058","DOIUrl":"10.1016/j.ejco.2023.100058","url":null,"abstract":"<div><p>In this paper, two new algorithms for dual decomposition-based distributed optimization are presented. Both algorithms rely on the quadratic approximation of the dual function of the primal optimization problem. The dual variables are updated in each iteration through a maximization of the approximated dual function. The first algorithm approximates the dual function by solving a regression problem, based on the values of the dual function collected from previous iterations. The second algorithm updates the parameters of the quadratic approximation via a quasi-Newton scheme. Both algorithms employ step size constraints for the update of the dual variables. Furthermore, the subgradients from previous iterations are stored in order to construct cutting planes, similar to bundle methods for nonsmooth optimization. However, instead of using the cutting planes to formulate a piece-wise linear over-approximation of the dual function, they are used to construct valid inequalities for the update step. In order to demonstrate the efficiency of the algorithms, they are evaluated on a large set of constrained quadratic, convex and mixed-integer benchmark problems and compared to the subgradient method, the bundle trust method, the alternating direction method of multipliers and the quadratic approximation coordination algorithm. The results show that the proposed algorithms perform better than the compared algorithms both in terms of the required number of iterations and in the number of solved benchmark problems in most cases.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100058"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44992580","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}

{"title":"A mixed-integer exponential cone programming formulation for feature subset selection in logistic regression","authors":"Sahand Asgharieh Ahari ,&nbsp;Burak Kocuk","doi":"10.1016/j.ejco.2023.100069","DOIUrl":"10.1016/j.ejco.2023.100069","url":null,"abstract":"<div><p>Logistic regression is one of the widely-used classification tools to construct prediction models. For datasets with a large number of features, feature subset selection methods are considered to obtain accurate and interpretable prediction models, in which irrelevant and redundant features are removed. In this paper, we address the problem of <em>feature subset selection in logistic regression</em> using modern optimization techniques. To this end, we formulate this problem as a mixed-integer exponential cone program (MIEXP). To the best of our knowledge, this is the first time both nonlinear and discrete aspects of the underlying problem are fully considered within an exact optimization framework. We derive different versions of the MIEXP model by the means of regularization and goodness of fit measures including Akaike Information Criterion and Bayesian Information Criterion. Finally, we solve our MIEXP models using the solver <em>MOSEK</em> and evaluate the performance of our different versions over a set of toy examples and benchmark datasets. The results show that our approach is quite successful in obtaining accurate and interpretable prediction models compared to other methods from the literature.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100069"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44435816","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}

{"title":"A variational approach for supply chain networks with environmental interests","authors":"Gabriella Colajanni,&nbsp;Patrizia Daniele,&nbsp;Daniele Sciacca","doi":"10.1016/j.ejco.2023.100075","DOIUrl":"10.1016/j.ejco.2023.100075","url":null,"abstract":"<div><p>Nowadays, the supply chain networks, consisting of different tiers of decision-makers, provide an effective framework for the production, the distribution, and the consumption of goods. In this paper we propose a supply chain network optimization model where manufacturers, retailers and consumers in the demand markets have a degree of interest in environmental sustainability. The manufacturers can improve their energy level (assumed as variables), aim to minimize their environmental emissions (for production and transport) and can also establish the amount of quantity of the production waste to dispose in a eco-sustainable way. The retailers, who are also profit-maximizers, aim to minimize their environmental emissions (which depend on the chosen shipping methods). The consumers at demand markets make their own choices according to the prices and to their degree of aversion to the environmental emissions. We describe the behavior of each decision-maker and we present the mathematical model for each of them, deriving the variational inequality problems. Furthermore, we derive a unique variational inequality formulation for the entire network for whose solution an existence and uniqueness result is obtained. Finally, we illustrate some numerical simulations that highlight how the use of UAVs and the presence of waste sorting centers in the supply chain reduce environmental emissions and related costs.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100075"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46440929","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}

{"title":"On two symmetric Dai-Kou type schemes for constrained monotone equations with image recovery application","authors":"Kabiru Ahmed ,&nbsp;Mohammed Yusuf Waziri ,&nbsp;Abubakar Sani Halilu ,&nbsp;Salisu Murtala","doi":"10.1016/j.ejco.2023.100057","DOIUrl":"10.1016/j.ejco.2023.100057","url":null,"abstract":"<div><p>The Dai-Kou method Dai and Kou (2013), <span>[12]</span> is efficient for solving unconstrained optimization problems. However, its modified variants are quite rare for constrained nonlinear monotone equations. In an attempt to address this, two adaptive versions of the scheme with new and efficient parameter choices are presented in this paper. The schemes are obtained by analyzing eigenvalues of a modified Dai-Kou iteration matrix and constructing two new directions, which are used in the scheme's algorithms. The new methods are derivative-free, which is an attribute required for handling problems with very large dimensions. Both methods also satisfy the required condition for analyzing global convergence in the literature. By applying mild conditions, it is shown that the schemes are globally convergent and description of their effectiveness is achieved through experiments with four effective schemes for solving constrained nonlinear monotone equations. Furthermore, the methods are applied to recover images that are contaminated by impulse noise in compressed sensing.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100057"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46322938","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}

{"title":"The Marguerite Frank Award for the best EJCO paper 2022","authors":"Immanuel Bomze (Editor-in-Chief)","doi":"10.1016/j.ejco.2023.100065","DOIUrl":"https://doi.org/10.1016/j.ejco.2023.100065","url":null,"abstract":"","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100065"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49742545","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}