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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Tian, Zhuo Zhang, Changyou Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Large-scale finite element equations are usually solved by the Preconditioned Conjugate Gradient (PCG) iterative method, and the computational hotspots are sparse matrix-vector multiplication and inner product, which requires local and global communication. But, the latency of the high-performance cluster is too long for the PCG algorithm. This paper proposes a new asynchronous algorithm that could reduce the number of communication among cluster nodes to improve the scalability of finite element solvers. The performance could be improved up to 6.31x.

关键词： Finite element solver PCG Scalability asynchronous algorithm Communication

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Linear Convergence of asynchronous Gradient Push algorithm for Distributed Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2147-2159页

作者： Li, Huaqing Cheng, Huqiang Lu, Qingguo Wang, Zheng Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This article focuses on multiagent distributed asynchronous optimization over directed networks where each agent can only access its individual local function, and the aggregate aim is to minimize the cumulative sum of all local functions. Considering the asynchrony among the agents, we develop an algorithm in which agents compute and communicate individually, without any form of synchronized coordination. Agents perform their local updates by local communication with their immediate neighbors, and this may involve the use of stale information. Since asynchrony naturally leads to latency or packet loss, an asynchronous robust gradient tracking mechanism is developed to guarantee estimating the average of agents' gradients precisely. Moreover, it employs uncoordinated step-sizes which are more flexible and general than constant or decaying step-size. When the global objective is strongly convex and the local objectives have Lipschitz-continuous gradients, we prove that each agent executing the asynchronous algorithm linearly converges to the consensus optimal point at an O(lambda(k)) rate, where lambda is an element of (0, 1) is convergence factor and k represents the iteration number, with a step satisfying a tight explicit upper bound. Numerical experiments demonstrate that our algorithm has better advantages over the state-of-the-art asynchronous algorithms.

关键词： Convergence Optimization Protocols System recovery Linear programming Electronic mail Delays Vectors Synchronization Fasteners asynchronous algorithm delay distributed optimization linear convergence

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Gossip-based asynchronous algorithms for distributed composite optimization

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NEUROCOMPUTING 2025年 616卷

作者： Fang, Xianju Zhang, Baoyong Yuan, Deming Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

The distributed composite optimization problem associated a multi-agent network is investigated in this paper. Different from conventional optimization issues, the cost function of composite optimization consists of a convex function and a regularization function (possibly nonsmooth). The gossip protocol is also introduced to enhance the robustness of the network, and a gossip-based distributed composite mirror descent algorithm is presented to deal with the previous problem, which adopts the asynchronous communication method. Moreover, the algorithm performance is analyzed and the theoretical results on the corresponding error bounds are obtained. Finally, the distributed logistic regression is provided as an example to validate the practicability of the proposed algorithm.

关键词： asynchronous algorithm Composite optimization Distributed optimization Gossip protocol Mirror descent

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Distributed asynchronous Optimization With Inseparable Coupled Constraints and Its Application

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13458-13469页

作者： Wang, Ting Liu, Weihan Li, Zhongmei Ye, Zhencheng Peng, Xin Du, Wenli East China Univ Sci & Technol State Key Lab Ind Control Technol Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313099 Peoples R China

Considering the complexity of centralized plant-wide optimization and the presence of communication delay in production units, a distributed asynchronous optimization framework is developed for energy consumption optimization problem during ethylene production. First, the energy consumption optimization problem of ethylene process is formulated into a distributed asynchronous optimization problem. Then, considering multiple production units and the material transfer time involved, each unit is treated as a node, and each node is decomposed into calculation nodes, constraint nodes, and delay nodes. Specifically, each production unit can be optimized asynchronously without the necessity for synchronization. To overcome the impact of communication delay on asynchronous process, state vectors are incorporated into the distributed parameter projection algorithm. Additionally, to ensure that the inseparable coupled constraints between nodes during asynchronous operations are met, the parameter projection algorithm is employed. Numerical experiments and industrial simulations demonstrate the proposed algorithm exhibits faster convergence speeds compared to distributed synchronous algorithms, and the energy consumption is lower than the results obtained by the centralized algorithm. Note to Practitioners-As the scale of ethylene production expands, traditional centralized optimization methods struggle to meet the demands of plant-wide optimization due to their high model complexity and slow convergence rates. In this study, we develop a distributed optimization framework, in which the large-scale complex model is decomposed into small parts, and the global optimization task is accomplished cooperatively through local information exchange. Furthermore, considering the time delay due to material residence time in processing units, a distributed asynchronous parameter projection algorithm is proposed to solve the energy consumption issue in ethylene plant. Experimental results

关键词： Optimization Production Energy consumption Delays Convergence Separation processes Industries Heating systems Delay effects Automation Distributed optimization constraint node asynchronous algorithm ethylene separation process plant-wide optimization

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asynchronous algorithms for distributed optimisation and application to distributed regression with robustness to outliers

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IET CONTROL THEORY AND APPLICATIONS 2013年第17期7卷 2084-2089页

作者： Liu, Weikai Hua, Zeng Wuhan Inst Technol Sch Sci Wuhan 430073 Peoples R China

This study presents an asynchronous algorithm for distributed constrained optimisation problems in networks of agents. The iterative optimisation algorithm maintains a local estimate at each node and depends on local gradient or gradient-like updates in combination with a consensus policy, where an agent averages its own value with a current or outdated value of another. This asynchronous scheme does not require that agents exchange state information frequently, so it is more energy-efficient and more realistic than the synchronous one. Moreover, the proposed algorithm is fully distributed, that is, all agents only share data with their neighbours through local broadcasts. The proposed algorithm is applied to a distributed regression problem with robustness to outliers in sensor networks. Simulation results are provided to demonstrate the validity and superiority of the proposed scheme.

关键词： constraint handling gradient methods multi-agent systems multi-robot systems network theory (graphs) optimisation regression analysis asynchronous algorithm distributed regression problem outlier robustness distributed constrained optimisation problem iterative optimisation algorithm local gradient update gradient-like update consensus policy sensor network agent network

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A Distributed Proximal Alternating Direction Multiplier Method for Multiblock Nonsmooth Composite Optimization

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 202-215页

作者： Zhou, Yuan Guo, Luyao Shi, Xinli Cao, Jinde Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

In this article, we address a composite optimization problem in a distributed network. Each agent in the network possesses a private local convex function consisting of a differentiable term, a nonsmooth term, and a nonsmooth term combined with a linear operator. The objective is to minimize the sum of all local functions while achieving consensus among the local states through information exchange with neighboring agents. To tackle this problem, we propose a novel distributed proximal alternating direction multiplier method (ADMM). By introducing the proximal operator of the nonsmooth term, linearizing the smooth term, and incorporating an additional proximal term, the ADMM subproblem can be solved more efficiently. One key advantage of the proposed algorithm is that it allows each agent to select parameters without being constrained by the network topology. In some instances, the algorithm can be transformed into some classical optimization algorithms. The algorithm is further extended to an asynchronous version by introducing randomized block coordinate. We further analyze the convergence of the proposed asynchronous algorithm and establish the sublinear convergence rate under synchronous conditions. Finally, several numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

关键词： Optimization Convergence Artificial neural networks Linear programming Convex functions Machine learning algorithms Vectors asynchronous algorithm distributed composite optimization proximal alternating direction multiplier method (ADMM)

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asynchronous Incremental Stochastic Dual Descent algorithm for Network Resource Allocation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第9期66卷 2229-2244页

作者： Bedi, Amrit Singh Rajawat, Ketan IIT Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

Stochastic network optimization problems entail finding resource allocation policies that are optimum on an average but must be designed in an online fashion. Such problems are ubiquitous in communication networks, where resources such as energy and bandwidth are divided among nodes to satisfy certain long-term objectives. This paper proposes an asynchronous incremental dual decent resource allocation algorithm that utilizes delayed stochastic gradients for carrying out its updates. The proposed algorithm is well-suited to heterogeneous networks as it allows the computationally-challenged or energy-starved nodes to, at times, postpone the updates. The asymptotic analysis of the proposed algorithm is carried out, establishing dual convergence under both, constant and diminishing step sizes. It is also shown that with constant step size, the proposed resource allocation policy is asymptotically near-optimal. An application involving multicell coordinated beamforming is detailed, demonstrating the usefulness of the proposed algorithm.

关键词： Stochastic subgradient resource allocation asynchronous algorithm incremental algorithm

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Distributed Constrained Optimization Over Unbalanced Directed Networks Using asynchronous Broadcast-Based algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第3期66卷 1102-1115页

作者： Li, Huaqing Lu, Qingguo Chen, Guo Huang, Tingwen Dong, Zhaoyang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. Furthermore, we particularly discuss the scenario in which the interactions among agents over the whole network are subjected to possible link failures. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures. Under two standard assumptions that the communication network is directly strongly connected and the subgradients of all local objective functions are bounded, we provide an explicit analysis for convergence of the algorithm. Simulation results obtained by three numerical experiments substantiate the feasibility of the algorithm and validate the theoretical findings.

关键词： Signal processing algorithms Convex functions Linear programming Convergence Distributed algorithms Cost function asynchronous algorithm distributed convex optimization epigraph form inequality constraints unbalanced directed networks

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Event-triggered asynchronous distributed optimization algorithm with heterogeneous time-varying step-sizes

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 6175-6184页

作者： Xie, Tangtang Chen, Guo Liao, Xiaofeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

This paper concerns distributed convex optimization problems over time-varying undirected graphs, in which the global objective function is expressed as the sum of individual objective functions of the agents. Each agent only knows its local objective functions. To figure out such problems, an event-triggered asynchronous distributed optimization algorithm (termed as EV-ADOA) with time-varying heterogeneous step-sizes is proposed, which is suitable for undirected graphs changing over time. Under two standard assumptions on strongly convex and smoothness of local objective functions, the EV-ADOA can achieve linear convergence with a proper upper bound of the heterogeneous time-varying step-sizes. EV-ADOA with event-triggered scheme can decrease network communication, and the Zeno-like behavior strictly is excluded. The efficiency of EV-ADOA is demonstrated by experiments.

关键词： Distributed optimization asynchronous algorithm Event-triggered scheme Heterogeneous time-varying step-sizes Linear convergence

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asynchronous DISTRIBUTED ALTERNATING DIRECTION METHOD OF MULTIPLIERS: algorithm AND CONVERGENCE ANALYSIS 41

ASYNCHRONOUS DISTRIBUTED ALTERNATING DIRECTION METHOD OF MUL...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chang, Tsung-Hui Hong, Mingyi Liao, Wei-Cheng Wane, Xiangfeng Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Iowa State Univ Dept IMSE & ECE Ames IA 50011 USA Univ Minnesota Dept Elect & Compt Engn Minneapolis MN 55455 USA East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China

ISBN: (纸本)9781479999880

Alternating direction method of multipliers (ADMM) has been recognized as an efficient approach for solving many large-scale learning problems over a computer cluster. However, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest workers. In this paper, we propose an asynchronous distributed ADMM (AD-ADMM) which can effectively improve the time efficiency of distributed optimization. Our main interest lies in characterizing the convergence conditions of the AD-ADMM, under the popular partially asynchronous model which is defined based on a maximum tolerable delay in the network. Specifically, by considering general and possibly non-convex cost functions, we show that the AD-ADMM converges to the set of Karush-Kuhn-Tucker (KKT) points as long as the algorithm parameters are chosen appropriately according to the network delay. We also show that the asynchrony of ADMM has to be handled with care, as a slightly different implementation can significantly jeopardize the algorithm convergence.

关键词： Distributed optimization alternating direction method of multipliers asynchronous algorithm

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