EURO Journal on Computational Optimization最新文献_第5页

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 Epub Date: 2025-06-06 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 Epub Date: 2025-07-17 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

Modern optimization approaches to classification—Special issue editorial 分类的现代优化方法--特刊社论

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-07-14 DOI: 10.1016/j.ejco.2024.100097

António Pedro Duarte Silva , Laura Palagi , Veronica Piccialli

引用次数: 0

New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan 短寿命产品的综合生产和配送调度问题的新计算结果

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-06-20 DOI: 10.1016/j.ejco.2024.100095

Markó Horváth

{"title":"New computational results for integrated production and outbound distribution scheduling problems for a product with a short lifespan","authors":"Markó Horváth","doi":"10.1016/j.ejco.2024.100095","DOIUrl":"https://doi.org/10.1016/j.ejco.2024.100095","url":null,"abstract":"<div><p>In this paper, we consider an integrated production and outbound distribution scheduling problem with a single production site, and its extension to multiple plants. A set of orders must be satisfied such that the required pieces from a single product must be first processed on a single machine in a plant, then must be delivered to the customers before their lifespan expire using a single vehicle. The goal is to minimize the makespan of the solution, which is the return time of the vehicle after its last trip. We propose an elementary variable neighborhood search to solve the problem, using two new local search operators. Our computational results show that this simple procedure outperforms the existing, sometimes complex approaches on the widely used benchmark dataset. We also review the existing computational results, and demonstrate that in some cases the comparisons in the literature are invalid due to the use of different rounding of the data. By re-evaluating the accessible solutions we provide a fair comparison for each rounding method. We also consider the extension of the problem to multiple plants, and adapt our solution approach for this extension. Our experiments show that our method is competitive in terms of solution quality with the existing solution approach for the problem.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100095"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000121/pdfft?md5=cf7e3d7b2aaa208cbc58be5f8cb0cff9&pid=1-s2.0-S2192440624000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483110","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

An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems 解决大规模电力系统中网络受限随机机组承诺问题的有效混合分解方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-02-05 DOI: 10.1016/j.ejco.2024.100085

Ricardo M. Lima , Gonzalo E. Constante-Flores , Antonio J. Conejo , Omar M. Knio

{"title":"An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems","authors":"Ricardo M. Lima , Gonzalo E. Constante-Flores , Antonio J. Conejo , Omar M. Knio","doi":"10.1016/j.ejco.2024.100085","DOIUrl":"https://doi.org/10.1016/j.ejco.2024.100085","url":null,"abstract":"<div><p>We propose a novel hybrid method to solve the network-constrained stochastic unit commitment problem. We target realistic large-scale instances including hundreds of thermal generation units, thousands of transmission lines and nodes, and a large number of stochastic renewable generation units. This scheduling problem is formulated as a two-stage stochastic programming problem with continuous and binary variables in the first stage and only continuous variables in the second stage. We develop a hybrid solution method that decomposes the original problem into a master problem including unit commitment and dispatch decisions, and decomposed subproblems representing dispatch with transmission constraints per scenario. The proposed decomposition embeds a column-and-constraint generation step within the traditional Benders decomposition framework. The performance of the proposed decomposition technique is contrasted with the solution of the extensive form via branch-and-cut and Benders decomposition available in commercial solvers, and with conventional Benders decomposition variants. Our computational experiments show that the proposed method generates bounds of superior quality and finds solutions for instances where other approaches fail.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100085"},"PeriodicalIF":2.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000029/pdfft?md5=6e5b9d1b9d47dcb072bff8ae23d37d2b&pid=1-s2.0-S2192440624000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714415","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

Communication-efficient ADMM using quantization-aware Gaussian process regression 使用量化感知高斯过程回归的通信高效 ADMM

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-09-13 DOI: 10.1016/j.ejco.2024.100098

Aldo Duarte , Truong X. Nghiem , Shuangqing Wei

{"title":"Communication-efficient ADMM using quantization-aware Gaussian process regression","authors":"Aldo Duarte , Truong X. Nghiem , Shuangqing Wei","doi":"10.1016/j.ejco.2024.100098","DOIUrl":"10.1016/j.ejco.2024.100098","url":null,"abstract":"<div><p>In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents' local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, due to data quantization, the GP algorithm is modified to account for the introduced quantization noise statistics. We further improve our approach by introducing an orthogonalization process to the quantizer's input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer's introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100098"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440624000157/pdfft?md5=8fd7b21c89e782f11b9739c488b6329b&pid=1-s2.0-S2192440624000157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238586","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

Finding quadratic underestimators for optimal value functions of nonconvex all-quadratic problems via copositive optimization 利用组合优化方法寻找非凸全二次问题最优值函数的二次低估量

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-11-26 DOI: 10.1016/j.ejco.2024.100100

Markus Gabl , Immanuel M. Bomze

{"title":"Finding quadratic underestimators for optimal value functions of nonconvex all-quadratic problems via copositive optimization","authors":"Markus Gabl , Immanuel M. Bomze","doi":"10.1016/j.ejco.2024.100100","DOIUrl":"10.1016/j.ejco.2024.100100","url":null,"abstract":"<div><div>Modeling parts of an optimization problem as an optimal value function that depends on a top-level decision variable is a regular occurrence in optimization and an essential ingredient for methods such as Benders Decomposition. It often allows for the disentanglement of computational complexity and exploitation of special structures in the lower-level problem that define the optimal value functions. If this problem is convex, duality theory can be used to build piecewise affine models of the optimal value function over which the top-level problem can be optimized efficiently. In this text, we are interested in the optimal value function of an all-quadratic problem (also called quadratically constrained quadratic problem, QCQP) which is not necessarily convex, so that duality theory can not be applied without introducing a generally unquantifiable relaxation error. This issue can be bypassed by employing copositive reformulations of the underlying QCQP. We investigate two ways to parametrize these by the top-level variable. The first one leads to a copositive characterization of an underestimator that is sandwiched between the convex envelope of the optimal value function and that envelope's lower-semicontinuous hull. The dual of that characterization allows us to derive affine underestimators. The second parametrization yields an alternative characterization of the optimal value function itself, which other than the original version has an exact dual counterpart. From the latter, we can derive convex and nonconvex quadratic underestimators of the optimal value function. In fact, we can show that any quadratic underestimator is associated with a dual feasible solution in a certain sense.</div></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100100"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748605","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

Recent advances in exact optimization methods for OR applications – Special issue editorial OR应用精确优化方法的最新进展-特刊社论

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-06-28 DOI: 10.1016/j.ejco.2024.100096

Markus Sinnl

引用次数: 0

Piecewise SOS-convex moment optimization and applications via exact semi-definite programs 通过精确半有限程序进行片断 SOS-凸矩优化及其应用

IF 2.6

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-06-19 DOI: 10.1016/j.ejco.2024.100094

Q.Y. Huang , V. Jeyakumar , G. Li

{"title":"Piecewise SOS-convex moment optimization and applications via exact semi-definite programs","authors":"Q.Y. Huang , V. Jeyakumar , G. Li","doi":"10.1016/j.ejco.2024.100094","DOIUrl":"https://doi.org/10.1016/j.ejco.2024.100094","url":null,"abstract":"<div><p>This paper presents exact Semi-Definite Program (SDP) reformulations for infinite-dimensional moment optimization problems involving a new class of piecewise Sum-of-Squares (SOS)-convex functions and projected spectrahedral support sets. These reformulations show that solving a single SDP finds the optimal value and an optimal probability measure of the original moment problem. This is done by establishing an SOS representation for the non-negativity of a piecewise SOS-convex function over a projected spectrahedron. Finally, as an application and a proof-of-concept illustration, the paper presents numerical results for the Newsvendor and revenue maximization problems with higher-order moments by solving their equivalent SDP reformulations. These reformulations promise a flexible and efficient approach to solving these models. The main novelty of the present work in relation to the recent research lies in finding the solution to moment problems, for the first time, with piecewise SOS-convex functions from their numerically tractable exact SDP reformulations.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"12 ","pages":"Article 100094"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S219244062400011X/pdfft?md5=a56c7d9f20401f8e18a8c36f5d31f0d6&pid=1-s2.0-S219244062400011X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483109","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

The Marguerite Frank Award for the best EJCO paper 2023 玛格丽特-弗兰克奖--表彰 2023 年最佳 EJCO 论文

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2024-01-01 Epub Date: 2024-03-18 DOI: 10.1016/j.ejco.2024.100087

Immanuel Bomze (Editor-in-Chief)

引用次数: 0