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distributed optimization Over Dependent Random Networks

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第8期68卷 4812-4826页

作者： Aghajan, Adel Touri, Behrouz Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA

We study the averaging-based distributed optimization solvers over random networks. We show a general result on the convergence of such schemes using weight matrices that are row-stochastic almost surely and column-stochastic in expectation for a broad class of dependent weight-matrix sequences. In addition to implying many of the previously known results on this domain, our work shows the robustness of distributed optimization results to link failure. Also, it provides a new tool for synthesizing distributed optimization algorithms. To prove our main theorem, we establish new results on the rate of convergence analysis of averaging dynamics over (dependent) random networks. These secondary results, along with the required martingale-type results to establish them, might be of interest to broader research endeavors in distributed computation over random networks.

关键词： Convex optimization directed graph distributed optimization random networks spanning tree

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distributed optimization in Prescribed-Time: Theory and Experiment

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第2期9卷 564-576页

作者： Gong, Xin Cui, Yukang Shen, Jun Xiong, Junlin Huang, Tingwen Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Shenzhen Univ Coll Mech & Control Engn Guangdong Key Lab Electromagnet Control & Intelli Shenzhen 518060 Peoples R China Shenzhen Univ Peng Cheng Lab Shenzhen 518000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Qatar Texas A&M Doha 23874 Qatar

This work studies the analysis and algorithm design problems of the distributed prescribed-time convex optimization of continuous-time multi-agent systems on undirected graphs. A new distributed algorithm with hybrid constant and time-specific update rates as well as predefined-time convergence guarantees is proposed, whose optimization period is independent of the initial states of all agents and the specific topology among agents. This algorithm consists of the following three cascading stages: 1) prescribed-time estimation of the average group gradients and Hessians over a time interval T-1;2) prescribed-time consensus of all agents over a time interval T-2;3) prescribed-time optimization to the global optimal point over a time interval T-3, where T-i > 0, i = 1 similar to 3, are three time intervals subject to T = T-1 + T-2 + T-3 with T denoting a predefined time interval. Accordingly, it is possible to pre-assign the settling time arbitrarily according to task requirements. The validity of this new algorithm and the practicability of given theoretical results are illustrated via a simulation example and a prescribed-time optimal rendezvous formation experiment of an indoor unmanned aerial vehicle swarm.

关键词： distributed optimization Multi-agent systems Optimal rendezvous formation Prescribed-time convergence

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distributed optimization of first-order discrete-time multi-agent systems with event-triggered communication

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NEUROCOMPUTING 2017年 235卷 255-263页

作者： Lu, Qingguo Li, Huaqing Xia, Dawen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Peoples R China Guizhou Minzu Univ Coll Natl Culture & Cognit Sci Guiyang 550025 Peoples R China

This paper focuses on the event-triggered distributed subgradient algorithms for solving a class of convex optimization problems based on first-order discrete-time multi-agent systems over undirected networks. The communication process of the whole network is controlled by a set of trigger conditions monitored by each agent. The trigger condition and event-triggered distributed subgradient optimization algorithm for each agent are completely decentralized and just rest with each agent's and its neighboring agents' individual states at the event-triggered sequence of themselves as well as each agent's local objective function. At each time instant, each agent updates its state by employing its own objective function and the states collected from itself and its neighboring agents at their separate event-triggered time instants. A sufficient cohdition for ensuring the consensus and reaching the optimization solution is established under the condition that the undirected network topology is connected and the design parameters are properly designed. Theoretical analysis shows that the event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converge to an optimal solution of the convex optimization problem. Simulation results validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.

关键词： Consensus distributed optimization Event-triggered control Multi-agent systems Discrete-time

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distributed optimization in predefined-time for multi-agent systems over a directed network

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INFORMATION SCIENCES 2022年 615卷 743-757页

作者： Zhou, Tingting Wu, Huaiqin Cao, Jinde Yanshan Univ Sch Sci Qinhuangdao 066001 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This paper is concerned with the distributed optimization problem (DOP) with equality constraints in predefined time for multi-agent systems (MASs) over a directed network. Firstly, a new principle with respect to the predefined-time convergence is developed for absolute continuous functions, which plays a key role in the consensus analysis later. Secondly, a novel control protocol is designed to address the solution of the considered DOP with convex objective functions. Thirdly, on the basis of Lyapunov functional approach and the proposed convergence principle, the predefined-time consensus condi-tions are achieved. Under the designed control protocol, the consensus state can reach fea-sible region in finite time, and subsequently converges to an optimal solution. Finally, the correctness of the theoretical analysis and the feasibility of the designed control strategy are verified by two illustrative simulations and an application example.(c) 2022 Elsevier Inc. All rights reserved.

关键词： distributed optimization Multi-agent systems Predefined-time consensus Lyapunov functional approach Directed networks

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distributed optimization for Distribution Grids With Stochastic DER Using Multi-Agent Deep Reinforcement Learning

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IEEE ACCESS 2021年 9卷 63059-63072页

作者： Al-Saffar, Mohammed Musilek, Petr Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Univ Hradec Kralove Dept Cybernet Hradec Kralove 50003 Czech Republic

This article develops a special decomposition methodology for the traditional optimal power flow which facilitates optimal integration of stochastic distributed energy resources in power distribution systems. The resulting distributed optimal power flow algorithm reduces the computational complexity of the conventional linear programming approach while avoiding the challenges associated with the stochastic nature of the energy resources and loads. It does so using machine learning algorithms employed for two crucial tasks. First, two proposed algorithms, Dynamic distributed Multi-Microgrid and Monte Carlo Tree Search based Reinforcement Learning, constitute dynamic microgrids of network nodes to confirm the electric power transaction optimality. Second, the optimal distributed energy resources are obtained by the proposed deep reinforcement learning method named Multi Leader-Follower Actors under Centralized Critic. It accelerates conventional linear programming approach by considering a reduced set of resources and their constraints. The proposed method is demonstrated through a real-time balancing electricity market constructed over the IEEE 123-bus system and enhanced using price signals based on distribution locational marginal prices. This application clearly shows the ability of the new approach to effectively coordinate multiple distribution system entities while maintaining system security constraints.

关键词： optimization Microgrids Heuristic algorithms Stochastic processes Power systems Real-time systems Convex functions distributed architecture distributed optimization Monte Carlo tree search multi-agent deep reinforcement learning optimal power flow

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distributed optimization for Graph Matching

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 4815-4828页

作者： Van Tran, Quoc Sun, Zhiyong Anderson, Brian D. O. Ahn, Hyo-Sung Gwangju Inst Sci & Technol Sch Mech Engn Gwangju 61005 South Korea Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands Australian Natl Univ Res Sch Elect Energy & Mat Engn Acton ACT 2601 Australia

Graph matching, or the determination of the vertex correspondences between a pair of graphs, is a crucial task in various problems in different science and engineering disciplines. This article aims to propose a distributed optimization approach for graph matching (GM) between two isomorphic graphs over multiagent networks. For this, we first show that for a class of asymmetric graphs, GM of two isomorphic graphs is equivalent to a convex relaxation where the set of permutation matrices is replaced by the set of pseudostochastic matrices. Then, we formulate GM as a distributed convex optimization problem with equality constraints and a set constraint, over a network of multiple agents. For arbitrary labelings of the vertices, each agent only has information about just one vertex and its neighborhood, and can exchange information with its neighbors. A projected primal-dual gradient method is developed to solve the constrained optimization problem, and globally exponential convergence of the agents' states to the optimal permutation is achieved. Finally, we illustrate the effectiveness of the algorithm through simulation examples.

关键词： optimization Nonhomogeneous media Task analysis Labeling Sun Digital twin Convergence Cyber-physical systems distributed optimization graph matching (GM)

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distributed optimization for Model Predictive Control of Linear Dynamic Networks With Control-Input and Output Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2011年第1期8卷 233-242页

作者： Camponogara, Eduardo Scherer, Helton F. Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040900 Florianopolis SC Brazil

A linear dynamic network is a system of subsystems that approximates the dynamic model of large, geographically distributed systems such as the power grid and traffic networks. A favorite technique to operate such networks is distributed model predictive control (DMPC), which advocates the distribution of decision-making while handling constraints in a systematic way. This paper contributes to the state-of-the-art of DMPC of linear dynamic networks in two ways. First, it extends a baseline model by introducing constraints on the output of the subsystems and by letting subsystem dynamics to depend on the state besides the control signals of the subsystems in the neighborhood. With these extensions, constraints on queue lengths and delayed dynamic effects can be modeled in traffic networks. Second, this paper develops a distributed interior-point algorithm for solving DMPC optimization problems with a network of agents, one for each subsystem, which is shown to converge to an optimal solution. In a traffic network, this distributed algorithm permits the subsystem of an intersection to be reconfigured by only coordinating with the subsystems in its vicinity.

关键词： Convex optimization distributed optimization interior-point methods linear systems model predictive control

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distributed optimization with hybrid linear constraints for multi-agent networks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第4期32卷 2069-2083页

作者： Zheng, Yanling Liu, Qingshan Wang, Miao Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Southeast Univ Jiangsu Prov Key Lab Networked Collect Intelligen Nanjing Peoples R China

This article investigates the distributed constrained optimization with hybrid linear constraints for multi-agent networks, in which all the agents collaboratively minimize the global objective function with a sum of convex local objective functions, while the constraints are more general with local and global restrictions on the agents. Based on matrix and graph theories, a discrete-time algorithm under distributed manner is designed to deal with the organized problems. In addition, the optimality of the presented algorithm is obtained under certain initial restriction for the agents. By virtue of a novel Lyapunov function and the optimal conditions, rigorous analysis shows the convergence of the multi-agent networks with undirected and connected graphs. Finally, two simulation examples are presented to validate the theoretical consequence.

关键词： convergence distributed optimization hybrid constraints multi-agent networks

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distributed optimization for structured programs and its application to energy management in a building district

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JOURNAL OF PROCESS CONTROL 2020年 89卷 11-21页

作者： Belluschi, Fabio Falsone, Alessandro Ioli, Daniele Margellos, Kostas Garatti, Simone Prandini, Maria Politecn Milan Piazza Leonardo da Vinci 32 I-20133 Milan Italy Univ Oxford Dept Engn Sci Parks Rd Oxford OX1 3PJ England

This paper deals with structured multi-agent optimization problems that involve coupled local and global decision variables. We propose an iterative distributed algorithm that explicitly accounts for this structure, and requires the agents to communicate only their tentative solutions for the global variables throughout iterations. Our approach extends to structured multi-agent optimization a proximal-based distributed methodology that has recently appeared in the literature. Privacy of local information is preserved and communication effort is reduced with respect to alternative distributed solutions where local and global optimization variables are grouped together and treated as a single decision vector. Multi-agent optimization problems with the considered structural properties appear in various contexts. In this paper, we apply our approach to energy management in a district where multiple buildings can communicate over a possibly time-varying network and aim at optimizing the use of shared and local resources. We illustrate the efficacy of the resulting distributed energy management algorithm by means of a detailed simulation study on a cooling problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： distributed optimization Proximal minimization Energy management Building control

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distributed optimization Using ALADIN for MPC in Smart Grids

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2021年第5期29卷 2142-2152页

作者： Jiang, Yuning Sauerteig, Philipp Houska, Boris Worthmann, Karl ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Tech Univ Ilmenau Inst Math D-98693 Ilmenau Germany

This article presents a distributed optimization algorithm tailored to solve optimization problems arising in smart grids. In detail, we propose a variant of the augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method, which comes along with global convergence guarantees for the considered class of linear-quadratic optimization problems. We establish local quadratic convergence of the proposed scheme and elaborate its advantages compared with the alternating direction method of multipliers (ADMM). In particular, we show that, at the cost of more communication, ALADIN requires fewer iterations to achieve the desired accuracy. Furthermore, it is numerically demonstrated that the number of iterations is independent of the number of subsystems. The effectiveness of the proposed scheme is illustrated by running both an ALADIN and an ADMM-based model predictive controller on a benchmark case study.

关键词： optimization Batteries Convergence Power demand Smart grids Benchmark testing distributed optimization model predictive control (MPC) smart grid

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