Optimization Methods and Software最新文献_第8页

An efficient hybrid conjugate gradient method for unconstrained optimization 一种有效的无约束优化混合共轭梯度法

Optimization Methods and Software Pub Date : 2022-02-17 DOI: 10.1080/10556788.2021.1998490

A. Ibrahim, P. Kumam, A. Kamandi, A. Abubakar

引用次数: 6

Optimal order multigrid preconditioners for the distributed control of parabolic equations with coarsening in space and time 空间和时间粗化抛物型方程分布控制的最优阶多网格预调节器

Optimization Methods and Software Pub Date : 2022-02-17 DOI: 10.1080/10556788.2021.2022145

Andrei Draganescu, M. Hajghassem

{"title":"Optimal order multigrid preconditioners for the distributed control of parabolic equations with coarsening in space and time","authors":"Andrei Draganescu, M. Hajghassem","doi":"10.1080/10556788.2021.2022145","DOIUrl":"https://doi.org/10.1080/10556788.2021.2022145","url":null,"abstract":"We devise multigrid preconditioners for linear-quadratic space-time distributed parabolic optimal control problems. While our method is rooted in earlier work on elliptic control, the temporal dimension presents new challenges in terms of algorithm design and quality. Our primary focus is on the cG(s)dG(r) discretizations which are based on functions that are continuous in space and discontinuous in time, but our technique is applicable to various other space-time finite element discretizations. We construct and analyse two kinds of multigrid preconditioners: the first is based on full coarsening in space and time, while the second is based on semi-coarsening in space only. Our analysis, in conjunction with numerical experiments, shows that both preconditioners are of optimal order with respect to the discretization in case of cG(1)dG(r) for r = 0, 1 and exhibit a suboptimal behaviour in time for Crank–Nicolson. We also show that, under certain conditions, the preconditioner using full space-time coarsening is more efficient than the one involving semi-coarsening in space, a phenomenon that has not been observed previously. Our numerical results confirm the theoretical findings.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114538229","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

Variants of the A-HPE and large-step A-HPE algorithms for strongly convex problems with applications to accelerated high-order tensor methods 求解强凸问题的A-HPE和大阶A-HPE算法的变体及其在加速高阶张量方法中的应用

Optimization Methods and Software Pub Date : 2022-02-14 DOI: 10.1080/10556788.2021.2022148

M. Marques Alves

引用次数: 12

Solution approaches for the vehicle routing problem with occasional drivers and time windows 具有随机驾驶员和时间窗的车辆路径问题的求解方法

Optimization Methods and Software Pub Date : 2022-02-14 DOI: 10.1080/10556788.2021.2022142

L. Pugliese, D. Ferone, P. Festa, F. Guerriero, Giusy Macrina

{"title":"Solution approaches for the vehicle routing problem with occasional drivers and time windows","authors":"L. Pugliese, D. Ferone, P. Festa, F. Guerriero, Giusy Macrina","doi":"10.1080/10556788.2021.2022142","DOIUrl":"https://doi.org/10.1080/10556788.2021.2022142","url":null,"abstract":"The efficient management of last-mile delivery is one of the main challenges faced by on-line retailers and logistic companies. The main aim is to offer personalized delivery services, that meet speed, flexibility, and control requirements and try to reduce environmental impacts as well. Crowd-sourced shipping is an emerging strategy that can be used to optimize the last-mile delivery process. The main idea is to deliver packages to customers with the aid of non-professional couriers, called occasional drivers. In this paper, we address the vehicle routing problem with occasional drivers, time window constraints and multiple deliveries. To handle this problem, we design some greedy randomized adaptive search procedures (GRASP). In order to assess the behaviour of the proposed algorithms, computational experiments are carried out on benchmark instances and new generated test sets. A comparison with previous published approaches, tailored for the problem at hand, is also provided. The numerical results are very encouraging and highlight the superiority, in terms of both efficiency and effectiveness, of the proposed GRASP algorithms.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114350151","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: 5

A quasi-Newton proximal bundle method using gradient sampling technique for minimizing nonsmooth convex functions 用梯度采样技术求非光滑凸函数的拟牛顿近端束法

Optimization Methods and Software Pub Date : 2022-02-14 DOI: 10.1080/10556788.2021.2023522

M. Maleknia, M. Shamsi

{"title":"A quasi-Newton proximal bundle method using gradient sampling technique for minimizing nonsmooth convex functions","authors":"M. Maleknia, M. Shamsi","doi":"10.1080/10556788.2021.2023522","DOIUrl":"https://doi.org/10.1080/10556788.2021.2023522","url":null,"abstract":"This study aims to merge the well-established ideas of bundle and Gradient Sampling (GS) methods to develop an algorithm for locating a minimizer of a nonsmooth convex function. In the proposed method, with the help of the GS technique, we sample a number of differentiable auxiliary points around the current iterate. Then, by applying the standard techniques used in bundle methods, we construct a polyhedral (piecewise linear) model of the objective function. Moreover, by performing quasi-Newton updates on the set of auxiliary points, this polyhedral model is augmented with a regularization term that enjoys second-order information. If required, this initial model is improved by the techniques frequently used in GS and bundle methods. We analyse the global convergence of the proposed method. As opposed to the original GS method and some of its variants, our convergence analysis is independent of the size of the sample. In our numerical experiments, various aspects of the proposed method are examined using a variety of test problems. In particular, in contrast with many variants of bundle methods, we will see that the user can supply gradients approximately. Moreover, we compare the proposed method with some efficient variants of GS and bundle methods.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124663170","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: 1

Improved high-dimensional regression models with matrix approximations applied to the comparative case studies with support vector machines 改进的高维回归模型与矩阵近似应用于支持向量机的比较案例研究

Optimization Methods and Software Pub Date : 2022-02-14 DOI: 10.1080/10556788.2021.2022144

M. Roozbeh, S. Babaie-Kafaki, Z. Aminifard

{"title":"Improved high-dimensional regression models with matrix approximations applied to the comparative case studies with support vector machines","authors":"M. Roozbeh, S. Babaie-Kafaki, Z. Aminifard","doi":"10.1080/10556788.2021.2022144","DOIUrl":"https://doi.org/10.1080/10556788.2021.2022144","url":null,"abstract":"Nowadays, high-dimensional data appear in many practical applications such as biosciences. In the regression analysis literature, the well-known ordinary least-squares estimation may be misleading when the full ranking of the design matrix is missed. As a popular issue, outliers may corrupt normal distribution of the residuals. Thus, since not being sensitive to the outlying data points, robust estimators are frequently applied in confrontation with the issue. Ill-conditioning in high-dimensional data is another common problem in modern regression analysis under which applying the least-squares estimator is hardly possible. So, it is necessary to deal with estimation methods to tackle these problems. As known, a successful approach for high-dimension cases is the penalized scheme with the aim of obtaining a subset of effective explanatory variables that predict the response as the best, while setting the other parameters to zero. Here, we develop several penalized mixed-integer nonlinear programming models to be used in high-dimension regression analysis. The given matrix approximations have simple structures, decreasing computational cost of the models. Moreover, the models are effectively solvable by metaheuristic algorithms. Numerical tests are made to shed light on performance of the proposed methods on simulated and real world high-dimensional data sets.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121494629","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: 7

Stochastic block projection algorithms with extrapolation for convex feasibility problems 凸可行性问题的外推随机块投影算法

Optimization Methods and Software Pub Date : 2022-02-13 DOI: 10.1080/10556788.2021.1998492

I. Necoara

{"title":"Stochastic block projection algorithms with extrapolation for convex feasibility problems","authors":"I. Necoara","doi":"10.1080/10556788.2021.1998492","DOIUrl":"https://doi.org/10.1080/10556788.2021.1998492","url":null,"abstract":"The stochastic alternating projection (SP) algorithm is a simple but powerful approach for solving convex feasibility problems. At each step, the method projects the current iterate onto a random individual set from the intersection. Hence, it has simple iteration, but, usually, convergences slowly. In this paper, we develop accelerated variants of basic SP method. We achieve acceleration using two ingredients: blocks of sets and adaptive extrapolation. We propose SP-based algorithms based on extrapolated iterations of convex combinations of projections onto block of sets. Our approach is based on several new ideas and tools, including stochastic selection rules for the blocks, stochastic conditioning of feasibility problem, and novel strategies for designing adaptive extrapolated stepsizes. We prove that, under linear regularity of the sets, our stochastic block projection algorithms converge linearly in expectation, with a rate depending on the condition number of the (block) feasibility problem and on the size of the blocks. Otherwise, we prove that our methods converge sublinearly. Our convergence analysis reveals that such algorithms are most effective when a good sampling of the sets into well-conditioned blocks is given. The convergence rates also explain when algorithms combining block projections with adaptive extrapolation work better than their nonextrapolated variants.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672742","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: 3

A dual active-set proximal Newton algorithm for sparse approximation of correlation matrices 相关矩阵稀疏逼近的对偶活动集近端牛顿算法

Optimization Methods and Software Pub Date : 2022-02-13 DOI: 10.1080/10556788.2021.1998491

X. Liu, Chungen Shen, Li Wang

引用次数: 0

An efficient semismooth Newton method for adaptive sparse signal recovery problems 自适应稀疏信号恢复问题的有效半光滑牛顿方法

Optimization Methods and Software Pub Date : 2021-11-24 DOI: 10.1080/10556788.2022.2120983

Yanyun Ding, Haibin Zhang, P. Li, Yunhai Xiao

{"title":"An efficient semismooth Newton method for adaptive sparse signal recovery problems","authors":"Yanyun Ding, Haibin Zhang, P. Li, Yunhai Xiao","doi":"10.1080/10556788.2022.2120983","DOIUrl":"https://doi.org/10.1080/10556788.2022.2120983","url":null,"abstract":"We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between - and -norm as a regularization always has superior performance. In this paper, we consider an adaptive - model where the -norm with measures the data fidelity and the -term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the non-convex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm.","PeriodicalId":124811,"journal":{"name":"Optimization Methods and Software","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116904701","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

Distributionally robust expected residual minimization for stochastic variational inequality problems 随机变分不等式问题的分布鲁棒期望残差最小化

Optimization Methods and Software Pub Date : 2021-11-15 DOI: 10.1080/10556788.2023.2167995

A. Hori, Yuya Yamakawa, N. Yamashita

引用次数: 0