作者：
Zeng, XianlinLei, JinlongChen, JieBeijing Inst Technol
Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Tongji Univ
Shanghai Res Inst Intelligent Autonomous Syst Dept Control Sci & Engn Shanghai 200070 Peoples R China
This article develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. This article analyzes critica...
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This article develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. This article analyzes critical values for parameters in the proposed method and prove that the rate of convergence in terms of the duality gap function is O((1)/(t)2 ) by choosing suitable parameters. As far as we know, this is the first continuous-time primal dual accelerated method that can obtain the optimal rate. Then, this article applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method.
An articulation point is a node whose removal partitions the network into disconnected segments. The articulation points may affect the reliability and efficiency of wireless multi hop networks from different aspects....
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An articulation point is a node whose removal partitions the network into disconnected segments. The articulation points may affect the reliability and efficiency of wireless multi hop networks from different aspects. Although all articulation points destroy the connectivity of the network, their negative impact on the network is not equal. Removing some articulation points may disconnect a large subset of nodes or generate a large number of partitions, while removing some other articulation points may only disconnect a few nodes. In this paper, we present two novel problems for identifying the most vital articulation points that significantly impact the network. The first problem is finding the p most important articulation points that minimize the largest connected component in the remaining network. The second problem is finding the p most important articulation points whose removal maximizes the number of partitions in the network. We prove that both problems are NP-Hard and propose a distributed algorithm to identify the vital articulation points in both problems. The proposed algorithm establishes a distributed depth-first search tree to identify the articulation points, assigns a score to each articulation point, and selects the prominent articulation points based on their scores. We compare the proposed algorithm with a brute force-based exact algorithm. The simulation result shows that after removing the detected prominent articulation points by the proposed algorithm, the maximum difference between the largest partition size and the number of partitions with the optimal solutions are less than 27.6% and 28.2%, respectively, while the sent bytes of the proposed algorithm can be 89.9% lower.
In this article, we develop a new algorithm, named federated consensus-based algorithm (FCB), for sparse recovery, and show its performance in terms of both support recovery and signal recovery. Specifically, FCB is d...
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In this article, we develop a new algorithm, named federated consensus-based algorithm (FCB), for sparse recovery, and show its performance in terms of both support recovery and signal recovery. Specifically, FCB is designed on the basis of the federated computational architecture, to increase the computational parallelism and accelerate the convergence. The algorithm design is realized by integrating accelerated projection-based consensus (APC) with greedy techniques. Then, the conditions of exact support recovery and an upper bound of signal recovery error are derived for FCB in the noisy case. From the explicit expression of the signal recovery error bound, it is confirmed that FCB can stably recover sparse signals under appropriate conditions using the coherence statistic of the measurement matrix and the minimum magnitude of nonzero elements of the signal. Experimental results illustrate the performance of FCB, validating our derived conditions of exact support recovery and upper bound of signal recovery error. In summary, FCB utilizes the federated computational architecture, enabling high parallelism and fast convergence, and uses greedy techniques to guarantee stable recovery performance.
This article focuses on the synchronization control of networked uncertain parabolic partial differential equations (PDEs) with uncertain nonlinear actuator dynamics. Compared to existing networked PDE systems, contro...
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This article focuses on the synchronization control of networked uncertain parabolic partial differential equations (PDEs) with uncertain nonlinear actuator dynamics. Compared to existing networked PDE systems, control input occurs in ordinary differential equation (ODE) subsystems rather than in PDE ones. Compared to existing results, where the exact system parameters must be known for the entire system, this paper further considers parabolic PDE-ODE systems with unknown parameters affecting the interior of the PDE domain. Due to the unknown parameters and uncertain nonlinear actuator dynamics, the existing distributed algorithms and stability analysis tools cannot be utilized to solve the synchronization problem of cascaded parabolic systems. To address this difficulty, this study designs a novel passive identifier to estimate the states and unknown parameters. Subsequently, based on the passive identifier and Lyapunov function method, a synchronization controller is presented for cascaded parabolic PDE systems to ensure that the synchronization control and the boundedness of all the closed-loop signals are achieved. Lastly, the effectiveness of the obtained results is illustrated using simulation.
By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the en...
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By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove to be unfair. In this work, we analyze the outcomes of three relevant game-theoretical billing methods, which are innovatively transposed to the day-ahead scheduling of energy exchange within a liberalized residential community dominated by distributed energy resources. The first two approaches rely on a (static) daily billing scheme, while the third considers a multi-temporal (continuous) billing. The Nash equilibria are computed using distributed algorithms, hence ensuring individual decision-making and avoiding third-party dependencies. The cost distributions are assessed using both a qualitative and a quantitative comparison based on various prosumer profiles in a modern smart grid. It is shown that, depending on the billing option, either the contribution towards the entity (i.e., the ability to improve the global solution) or the individual empowerment (i.e., the ability to bargain) can be preferentially incentivized.
distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and a...
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distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and analyzing new algorithms for various applications. In this work, we provide a fresh perspective to understand, analyze, and design distributed optimization algorithms. Through the lens of multirate feedback control, we show that a wide class of distributed algorithms, including popular decentralized/federated schemes, can be viewed as discretizing a certain continuous-time feedback control system, possibly with multiple sampling rates, such as decentralized gradient descent, gradient tracking, and federated averaging. This key observation not only allows us to develop a generic framework to analyze the convergence of the entire algorithm class, but, more importantly, it also leads to an interesting way of designing new distributed algorithms. We develop the theory behind our framework and provide examples to highlight how the framework can be used in practice.
Emerging applications in the Internet of Things (IoT) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for ...
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Emerging applications in the Internet of Things (IoT) and edge computing/learning have sparked massive renewed interest in developing distributed versions of existing (centralized) iterative algorithms often used for optimization or machine learning purposes. While existing work in the literature exhibits similarities, for the tasks of both algorithm design and theoretical analysis, there is still no unified method or framework for accomplishing these tasks. This article develops such a general framework for distributing the execution of (centralized) iterative algorithms over networks in which the required information or data is partitioned between the nodes in the network. This article furthermore shows that the distributed iterative algorithm, which results from the proposed framework, retains the convergence properties (rate) of the original (centralized) iterative algorithm. In addition, this article applies the proposed general framework to several interesting example applications, obtaining results comparable to the state of the art for each such example, while greatly simplifying and generalizing their convergence analysis. These example applications reveal new results for distributed proximal versions of gradient descent, the heavy ball method, and Newton's method. For example, these results show that the dependence on the condition number for the convergence rate of this distributed heavy ball method is at least as good as that of centralized gradient descent.
We address the generalized Nash equilibrium seeking problem for a population of agents playing aggregative games with affine coupling constraints. We focus on semi-decentralized communication architectures, where ther...
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We address the generalized Nash equilibrium seeking problem for a population of agents playing aggregative games with affine coupling constraints. We focus on semi-decentralized communication architectures, where there is a central coordinator able to gather and broadcast signals of aggregative nature to the agents. By exploiting the framework of monotone operator theory and operator splitting, we first critically review the most relevant available algorithms and then design two novel schemes: 1) a single-layer, fixed-step algorithm with convergence guarantee for general (noncocoercive, nonstrictly) monotone aggregative games and 2) a single-layer proximal-type algorithm for a class of monotone aggregative games with linearly coupled cost functions. We also design novel accelerated variants of the algorithms via (alternating) inertial and over-relaxation steps. Finally, we show via numerical simulations that the proposed algorithms outperform those in the literature in terms of convergence speed.
In this article, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. The proposed m...
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In this article, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of "stable" and "fair" bilateral contracts which encourages prosumer participation. The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.
Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that...
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Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that |R| = |F|, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible graph discretizing the Euclidean plane only the standard square grid has been considered in the context of the classical OBLOT model. However, it is natural to consider also the other regular tessellation graphs, that are triangular and hexagonal grids. In particular, the former can be considered as the most general in terms of possible symmetries and *** provide a resolution algorithm for APF when the initial configuration is asymmetric and the considered topology is any regular tessellation graph. The algorithm and its correctness are based on a rigorous methodology.(c) 2022 Elsevier B.V. All rights reserved.
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