检索结果-内蒙古大学图书馆

A stochastic primal-dual algorithm for composite optimization with a linear operator

EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Wen, Meng Zhang, Yongqiang Tang, Yuchao Cui, Angang Peng, Jigen Xian Polytech Univ Sch Sci Xian 710048 Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Peoples R China Xian Shiyou Univ Coll Mat Sci & Engn Xian 710065 Peoples R China Yulin Univ Sch Math & Stat Yulin 719000 Peoples R China

This paper introduces a stochastic primal-dual algorithm tailored for solving optimization problems involving the sum of three functions with a linear operator. Additionally, we conduct a comprehensive analysis of the convergence of our proposed algorithm within a generally convex framework. Our study includes numerical experiments focusing on fused logistic regression and graph-guided regularized logistic regression problems. The results demonstrate that our algorithm outperforms other state-of-the-art methods in terms of efficiency and consistency.

关键词： Compositely optimization primal-dual algorithm Stochastic algorithm Three-operator splitting

来源：评论

学校读者我要写书评

暂无评论

primal-dual algorithm for distributed constrained optimization

引用

SYSTEMS & CONTROL LETTERS 2016年第0期96卷 110-117页

作者： Lei, Jinlong Chen, Han-Fu Fang, Hai-Tao Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100864 Peoples R China

The paper studies a distributed constrained optimization problem, where multiple agents connected in a network collectively minimize the sum of individual objective functions subject to a global constraint being an intersection of the local constraints assigned to the agents. Based on the augmented Lagrange method, a distributed primal-dual algorithm with a projection operation included is proposed to solve the problem. It is shown that with appropriately chosen constant step size,,the local estimates derived at all agents asymptotically reach a consensus at an optimal solution. In addition, the value of the cost function at the time-averaged estimate converges with rate O(1/k) to the optimal value for the unconstrained problem. By these properties, the proposed primal-dual algorithm is distinguished from the existing algorithms for distributed constrained optimization. The theoretical analysis is justified by numerical simulations. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Distributed constrained optimization primal-dual algorithm Augmented Lagrange method Multi-agent network

来源：评论

学校读者我要写书评

暂无评论

primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising

引用

JOURNAL OF ELECTRONIC IMAGING 2019年第4期28卷

作者： Liu, Xinwei Tang, Yuchao Yang, Yixuan Nanchang Univ Dept Math Nanchang Jiangxi Peoples R China

The variational method, which is a popular approach for image denoising, aims to estimate the original image from a noisy or corrupted image. To consider the constraints of image pixel values fully, our study investigates a constrained second-order total generalized variational (TGV) model, which includes non-negative and bounded constraints as a special case. By adopting an equivalent definition of the second-order TGV, we transform the proposed constrained minimization problem into a minimization of the sum of two convex functions, where one is composed of a linear transformation. Subsequently, we employ the relaxed primal-dual proximity algorithm to solve it. The advantage of the obtained algorithm is that it is matrix-inversion free and does not involve any subproblem. Numerical results demonstrate that the performance of the constrained TGV model is slightly better than that of the unconstrained model. (C) 2019 SPIE and IS&T

关键词： variational method total generalized variation primal-dual algorithm image denoising

来源：评论

学校读者我要写书评

暂无评论

A First-Order Stochastic primal-dual algorithm with Correction Step

引用

NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION 2017年第5期38卷 602-626页

作者： Rosasco, Lorenzo Villa, Silvia Bang Cong Vu Ist Italiano Tecnol Lab Computat & Stat Learning Genoa Italy MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Genoa Dept Informat Bioengn Robot & Syst Engn DIBRIS Genoa Italy Politecn Milan Dipartimento Matemat Milan Italy

In this article, we investigate the convergence properties of a stochastic primal-dual splitting algorithm for solving structured monotone inclusions involving the sum of a cocoercive operator and a composite monotone operator. The proposed method is the stochastic extension to monotone inclusions of a proximal method studied in the literature for saddle point problems. It consists in a forward step determined by the stochastic evaluation of the cocoercive operator, a backward step in the dual variables involving the resolvent of the monotone operator, and an additional forward step using the stochastic evaluation of the cocoercive operator introduced in the first step. We prove weak almost sure convergence of the iterates by showing that the primal-dual sequence generated by the method is stochastic quasi-Fejer-monotone with respect to the set of zeros of the considered primal and dual inclusions. Additional results on ergodic convergence in expectation are considered for the special case of saddle point models.

关键词： Cocoercive operator composite operator duality monotone inclusion maximal monotone operator operator splitting primal-dual algorithm stochastic errors

来源：评论

学校读者我要写书评

暂无评论

A fractional-order adaptive regularization primal-dual algorithm for image denoising

引用

INFORMATION SCIENCES 2015年第1期296卷 147-159页

作者： Tian, Dan Xue, Dingyu Wang, Dianhui Northeastern Univ Dept Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Shenyang Univ Dept Informat Engn Shenyang 110044 Liaoning Peoples R China La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

This paper aims to develop a fractional-order model and a primal-dual algorithm for image denoising, where a regularization parameter can be adjusted adaptively according to Morozov discrepancy principle at each iteration to ensure that the denoised image retains in a specific set. In the light of saddle-point theory, the convergence of our proposed algorithm is guaranteed. Simulations with comparisons are carried out to demonstrate the effectiveness of our proposed algorithm for image denoising. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Image denoising Fractional-order ROF model primal-dual algorithm Adaptive regularization parameter

来源：评论

学校读者我要写书评

暂无评论

Analysis of linear structured systems using a primal-dual algorithm

引用

SYSTEMS & CONTROL LETTERS 1996年第2期27卷 73-85页

作者： Hovelaque, V Commault, C Dion, JM ENSIEG INPG LAB AUTOMAT GRENOBLE F-38402 ST MARTIN DHERES FRANCE

In this paper, we give a polynomial algorithm to compute the infinite structure of a structured system. A directed graph is associated with such structured systems. The infinite zero orders can be computed on the associated graph via the determination of the minimal length of vertex disjoint input-output paths. This search corresponds to a minimum cost flow determination on an appropriate directed graph. The proposed algorithm is based on the primal-dual algorithm linked to linear programming. This polynomial algorithm is one of the most efficient for this type of problem. Moreover,. it allows an iterative determination of the generic infinite structure which is a key tool for solving numerous control problems.

关键词： linear systems structured systems infinite structure graph theory minimum cost flow primal-dual algorithm

来源：评论

学校读者我要写书评

暂无评论

A primal-dual algorithm for computing Finsler distances and applications

引用

CALCOLO 2024年第3期61卷 1-25页

作者： Ennaji, Hamza Queau, Yvain Elmoataz, Abderrahim Normandie Univ UNICAEN ENSICAEN CNRSGREYC Caen France

This note discusses the computation of the distance function with respect to Finsler metrics. To this end, we show how the Finsler variants of the Eikonal equation can be solved by a primal-dual algorithm exploiting the variational structure. We also discuss the acceleration of the algorithm by preconditioning techniques, and illustrate the flexibility of the proposed method through a series of numerical examples.

关键词： Finsler distances primal-dual algorithm Geodesics

来源：评论

学校读者我要写书评

暂无评论

A Distributed Proximal primal-dual algorithm for Energy Management With Transmission Losses in Smart Grid

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第11期18卷 7608-7618页

作者： Wang, Yifan Liu, Shuai Sun, Bo Li, Xiuxian Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201210 Peoples R China Tongji Univ Inst Adv Study Shanghai 201210 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

This article aims to address the problem of distributed energy management for both the generation and demand sides in smart grid. Different from many existing works, we investigate the SWM problem with transmission losses. In addition, instead of transforming the local constraint into an approximate penalty function or the projection set, we consider it as a convex nonsmooth indicator function from a different viewpoint. For such a composite problem consisting of smooth and nonsmooth terms, we propose a distributed proximal primal-dual algorithm based on dual decomposition and operator splitting techniques. Each node performs the algorithm through only local computation and communication with limited information, especially not sharing the sensitive gradient directly. It is also proved that the proposed algorithm leads to the global optima at a convergence rate O(1/k) with a fixed step-size. Several simulations verify the theoretical analysis and demonstrate the effectiveness of the proposed algorithm.

关键词： Energy management primal-dual algorithm proximal operator social welfare maximization (SWM) transmission losses

来源：评论

学校读者我要写书评

暂无评论

A primal-dual algorithm for computing a cost allocation in the core of economic lot-sizing games

引用

OPERATIONS RESEARCH LETTERS 2012年第6期40卷 453-458页

作者： Gopaladesikan, Mohan Uhan, Nelson A. Zou, Jikai Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Purdue Univ Dept Stat W Lafayette IN 47907 USA

We consider the economic lot-sizing game with general concave ordering cost functions. It is well-known that the core of this game is nonempty when the inventory holding costs are linear. The main contribution of this work is a combinatorial, primal-dual algorithm that computes a cost allocation in the core of these games in polynomial time. We also show that this algorithm can be used to compute a cost allocation in the core of economic lot-sizing games with remanufacturing under certain assumptions. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Cooperative games Cost allocation Economic lot-sizing primal-dual algorithm

来源：评论

学校读者我要写书评

暂无评论

A shifted-barrier primal-dual algorithm model for linearly constrained optimization problems

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 1999年第1-3期12卷 157-188页

作者： Di Pillo, G Lucidi, S Palagi, L Univ Rome La Sapienza Dipartimento Informat & Sistemist I-00185 Rome Italy

In this paper we describe a Newton-type algorithm model for solving smooth constrained optimization problems with nonlinear objective function, general linear constraints and bounded variables. The algorithm model is based on the definition of a continuously differentiable exact merit function that follows an exact penalty approach for the box constraints and an exact augmented Lagrangian approach for the general linear constraints. Under very mild assumptions and without requiring the strict complementarity assumption, the algorithm model produces a sequence of pairs {x(k), lambda(k)} converging quadratically to a pair ((x) over bar, <(lambda)over bar>) where (x) over bar satisfies the first order necessary conditions and <(lambda)over bar> is a KKT multipliers vector associated to the linear constraints. As regards the behaviour of the sequence {x(k)} alone, it is guaranteed that it converges at least superlinearly. At each iteration, the algorithm requires only the solution of a linear system that can be performed by means of conjugate gradient methods. Numerical experiments and comparison are reported.

关键词： linearly constrained optimization primal-dual algorithm Penalty-Lagrangian merit function

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：