EURO Journal on Computational Optimization最新文献_第8页

A simplified convergence theory for Byzantine resilient stochastic gradient descent 拜占庭弹性随机梯度下降的简化收敛理论

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100038

Lindon Roberts , Edward Smyth

引用次数: 1

Exponential extrapolation memory for tabu search 禁忌搜索的指数外推记忆

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100028

Håkon Bentsen, Arild Hoff, Lars Magnus Hvattum

{"title":"Exponential extrapolation memory for tabu search","authors":"Håkon Bentsen, Arild Hoff, Lars Magnus Hvattum","doi":"10.1016/j.ejco.2022.100028","DOIUrl":"10.1016/j.ejco.2022.100028","url":null,"abstract":"<div><p>Tabu search is a well-established metaheuristic framework for solving hard combinatorial optimization problems. At its core, the method uses different forms of memory to guide a local search through the solution space so as to identify high-quality local optima while avoiding getting stuck in the vicinity of any particular local optimum. This paper examines characteristics of moves that can be exploited to make good decisions about steps that lead away from recently visited local optima and towards a new local optimum. Our approach uses a new type of adaptive memory based on a construction called exponential extrapolation. The memory operates by means of threshold inequalities that ensure selected moves will not lead to a specified number of most recently encountered local optima. Computational experiments on a set of one hundred different benchmark instances for the binary integer programming problem suggest that exponential extrapolation is a useful type of memory to incorporate into a tabu search.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100028"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000041/pdfft?md5=d79a522b1d114e009dc737ac4d866cee&pid=1-s2.0-S2192440622000041-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126242208","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 reinforcement learning approach to the stochastic cutting stock problem 随机切削库存问题的强化学习方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100027

Anselmo R. Pitombeira-Neto , Arthur H.F. Murta

{"title":"A reinforcement learning approach to the stochastic cutting stock problem","authors":"Anselmo R. Pitombeira-Neto , Arthur H.F. Murta","doi":"10.1016/j.ejco.2022.100027","DOIUrl":"10.1016/j.ejco.2022.100027","url":null,"abstract":"<div><p>We propose a formulation of the stochastic cutting stock problem as a discounted infinite-horizon Markov decision process. At each decision epoch, given current inventory of items, an agent chooses in which patterns to cut objects in stock in anticipation of the unknown demand. An optimal solution corresponds to a policy that associates each state with a decision and minimizes the expected total cost. Since exact algorithms scale exponentially with the state-space dimension, we develop a heuristic solution approach based on reinforcement learning. We propose an approximate policy iteration algorithm in which we apply a linear model to approximate the action-value function of a policy. Policy evaluation is performed by solving the projected Bellman equation from a sample of state transitions, decisions and costs obtained by simulation. Due to the large decision space, policy improvement is performed via the cross-entropy method. Computational experiments are carried out with the use of realistic data to illustrate the application of the algorithm. Heuristic policies obtained with polynomial and Fourier basis functions are compared with myopic and random policies. Results indicate the possibility of obtaining policies capable of adequately controlling inventories with an average cost up to 80% lower than the cost obtained by a myopic policy.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100027"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S219244062200003X/pdfft?md5=135d32e50b9857c32c1577a7a14985fc&pid=1-s2.0-S219244062200003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89403511","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: 9

Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes 分散个性化联邦学习:所有个性化模式的下界和最优算法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100041

Abdurakhmon Sadiev , Ekaterina Borodich , Aleksandr Beznosikov , Darina Dvinskikh , Saveliy Chezhegov , Rachael Tappenden , Martin Takáč , Alexander Gasnikov

引用次数: 4

Twenty years of EUROPT, the EURO working group on Continuous Optimization 二十年的EUROPT，欧洲持续优化工作组

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100039

Sonia Cafieri , Tatiana Tchemisova , Gerhard-Wilhelm Weber

引用次数: 1

New neighborhoods and an iterated local search algorithm for the generalized traveling salesman problem 广义旅行商问题的新邻域及迭代局部搜索算法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100029

Jeanette Schmidt, Stefan Irnich

引用次数: 1

Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness 有限分布信息下的机会约束优化:基于抽样和分布鲁棒性的重新表述综述

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100030

Simge Küçükyavuz , Ruiwei Jiang

{"title":"Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness","authors":"Simge Küçükyavuz , Ruiwei Jiang","doi":"10.1016/j.ejco.2022.100030","DOIUrl":"10.1016/j.ejco.2022.100030","url":null,"abstract":"<div><p>Chance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only limited information on the distribution is available, such as a sample from the distribution, or the moments of the distribution. We first review recent developments in mixed-integer linear formulations of chance-constrained programs that arise from finite discrete distributions (or sample average approximation). We highlight successful reformulations and decomposition techniques that enable the solution of large-scale instances. We then review active research in distributionally robust CCP, which is a framework to address the ambiguity in the distribution of the random data. The focal point of our review is on scalable formulations that can be readily implemented with state-of-the-art optimization software. Furthermore, we highlight the prevalence of CCPs with a review of applications across multiple domains.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100030"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000065/pdfft?md5=9e123764c3bf29d9a58fa0f64cbc4b9a&pid=1-s2.0-S2192440622000065-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131035749","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: 15

Direct nonlinear acceleration 直接非线性加速度

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2022-01-01 DOI: 10.1016/j.ejco.2022.100047

Aritra Dutta , El Houcine Bergou , Yunming Xiao , Marco Canini , Peter Richtárik

{"title":"Direct nonlinear acceleration","authors":"Aritra Dutta , El Houcine Bergou , Yunming Xiao , Marco Canini , Peter Richtárik","doi":"10.1016/j.ejco.2022.100047","DOIUrl":"10.1016/j.ejco.2022.100047","url":null,"abstract":"<div><p>Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, such as regularized nonlinear acceleration (RNA) of Scieur et al. <span>[22]</span>, were proposed and shown to accelerate fixed point iterations. In contrast to RNA which computes extrapolation coefficients by (approximately) setting the gradient of the objective function to zero at the extrapolated point, we propose a more direct approach, which we call <em>direct nonlinear acceleration (DNA)</em>. In DNA, we aim to minimize (an approximation of) the function value at the extrapolated point instead. We adopt a regularized approach with regularizers designed to prevent the model from entering a region in which the functional approximation is less precise. While the computational cost of DNA is comparable to that of RNA, our direct approach significantly outperforms RNA on both synthetic and real-world datasets. While the focus of this paper is on convex problems, we obtain very encouraging results in accelerating the training of neural networks.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100047"},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000235/pdfft?md5=1af83969ee833bb0a8954f808f6ca4ee&pid=1-s2.0-S2192440622000235-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687887","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: 1

First-Order Methods for Convex Optimization 凸优化的一阶方法

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100015

Pavel Dvurechensky , Shimrit Shtern , Mathias Staudigl

{"title":"First-Order Methods for Convex Optimization","authors":"Pavel Dvurechensky , Shimrit Shtern , Mathias Staudigl","doi":"10.1016/j.ejco.2021.100015","DOIUrl":"10.1016/j.ejco.2021.100015","url":null,"abstract":"<div><p>First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important class of algorithms is motivated by the success stories reported in various applications, including most importantly machine learning, signal processing, imaging and control theory. First-order methods have the potential to provide low accuracy solutions at low computational complexity which makes them an attractive set of tools in large-scale optimization problems. In this survey, we cover a number of key developments in gradient-based optimization methods. This includes non-Euclidean extensions of the classical proximal gradient method, and its accelerated versions. Additionally we survey recent developments within the class of projection-free methods, and proximal versions of primal-dual schemes. We give complete proofs for various key results, and highlight the unifying aspects of several optimization algorithms.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"9 ","pages":"Article 100015"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440621001428/pdfft?md5=19763cbf839252d3f78a91ae92c0f36f&pid=1-s2.0-S2192440621001428-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128767137","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: 20

Conic optimization: A survey with special focus on copositive optimization and binary quadratic problems 二次优化:特别关注组合优化和二元二次问题的调查

IF 2.4

EURO Journal on Computational Optimization Pub Date : 2021-01-01 DOI: 10.1016/j.ejco.2021.100021

Mirjam Dür , Franz Rendl

引用次数: 16