The distributed convex optimisation issue with inequality constraints and box constraints on the directed communication topology is examined in this paper. In the case of the communication delay between agents, we pro...
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The distributed convex optimisation issue with inequality constraints and box constraints on the directed communication topology is examined in this paper. In the case of the communication delay between agents, we propose a piecewise zero-gradient-sum triggered control algorithm that allows arbitrary initial values. Using the log-barrier penalty method, we only need to study an unconstrained approximation problem of the original problem. Firstly, each agent's state converges to the optimal value of the related local objective function in a fixed time. Simultaneously, the upper bound of fixed time in this paper is smaller than some existing results. Then, in the case of communication delay, time-triggered and event-triggered methods with non-uniform sampling interval are proposed in this paper, which make the communication cost lower and the application broader. Using the Lyapunov function method, the sufficient conditions for each agent's state to converge to the optimal point are given, and the Zeno behaviour is excluded. Finally, to confirm the algorithm's effectiveness, we provide two numerical examples and one machine learning example as demonstrations.
The distributed convex optimization problem subject to time-varying communication delays and switching network topologies is addressed in this paper. Based on continuous-time zero-gradient-sum scheme, the novel distri...
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The distributed convex optimization problem subject to time-varying communication delays and switching network topologies is addressed in this paper. Based on continuous-time zero-gradient-sum scheme, the novel distributed algorithms are proposed to minimize the global objective function which is composed of a sum of strictly convex local cost functions. In the fixed network topology case, by constructing a new Lyapunov-Krasovskii function, two explicit sufficient conditions for the maximum admissible time delay are derived to guarantee that all agents' states converge to the optimal solution. In the switching network topology case, the stability condition is derived by the common Lyapunov function theory. In addition, two sufficient conditions about the maximum admissible time delays are also derived for the fixed and switching weight-balanced network topologies, respectively. Several simulation tests are used to illustrate the effectiveness of our obtained theoretical results.
Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed conve...
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Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.
The convex optimization problem of multi-agent systems is investigated. In order to deduce the communication burden of the system and simplify the implementation of the algorithm on the digital controllers, a sample-b...
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The convex optimization problem of multi-agent systems is investigated. In order to deduce the communication burden of the system and simplify the implementation of the algorithm on the digital controllers, a sample-based event-triggered optimization algorithm is proposed and a novel dynamic event-trigged condition is designed. The global optimal solution can be obtained exponentially. The event-triggered time intervals are significantly enlarged by dynamic event-triggered condition. Moreover, Zeno behavior is excluded due to sampling control mechanism in our scheme. Two numerical cases are presented to validate the relevant results.
The distributed optimization for multi-agent systems with time delay and first-order is investigated in this paper. The objective of the distributed optimization is to optimize the objective function composed of the s...
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The distributed optimization for multi-agent systems with time delay and first-order is investigated in this paper. The objective of the distributed optimization is to optimize the objective function composed of the sum of local objective functions, which can only be known by its corresponding agents. Firstly, a distributed algorithm for time-delay systems is proposed to solve the optimization problem that each agent depends on its own state and the state between itself and its neighbors. Secondly, Lyapunov-Krasovskii function is used to prove that the states of each agent can be asymptotically the same, and the states are optimal. Finally, an example is given for illustrating the analytical results and a comparison is also gave to illustrate the differences between the algorithm of this paper and other results.
The distributed optimisation problem with privacy-preserving properties is considered in this paper. To solve this problem, a zero-gradient-sum algorithm based on output mask is proposed. An event-triggered condition ...
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The distributed optimisation problem with privacy-preserving properties is considered in this paper. To solve this problem, a zero-gradient-sum algorithm based on output mask is proposed. An event-triggered condition is designed by using the output mask, which reduces the communication burden of the system effectively. The theoretical results show that the proposed algorithm can solve the optimisation problem, while the privacy information of nodes is preserved. In addition, the event-triggered condition designed based on the output mask can effectively avoid Zeno behaviour. Two simulation cases are performed to validate the effectiveness of the algorithm.
This paper extends the event-triggered communication in consensus problems of multi-agent systems to the case of distributed continuous-time convex optimization over weight-balanced digraphs. We address problems whose...
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ISBN:
(纸本)9789811023354;9789811023347
This paper extends the event-triggered communication in consensus problems of multi-agent systems to the case of distributed continuous-time convex optimization over weight-balanced digraphs. We address problems whose global objective functions are a sum of local functions associated to each agent. We utilize the event-triggered communication technique to reduce the communication load and avoid Zeno behavior meanwhile. Based on Lyapunov approach, we prove that the zero-gradient-sum (ZGS) algorithm combined with the event-triggered communication makes all agents' states converge to the optimal solution of the global objective function exponentially fast.
In this study, we investigate the distributed convex optimisation problem of the multi-agent system over an undirected network, in which the global objective function is the sum of all the local cost functions and the...
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In this study, we investigate the distributed convex optimisation problem of the multi-agent system over an undirected network, in which the global objective function is the sum of all the local cost functions and the local cost function of each agent is only known by itself. In order to save the computation and communication resources, the above optimisation problem is addressed by designing two zero-gradient-sum algorithms with state-based dynamic event-triggered mechanism, where the information communication only occurs at some discrete triggering time instants. The Zeno behaviour of the above event-triggered control scheme is excluded by the existence of the positive minimum inter-event time (MIET). The convergence is proved based on the Lyapunov method. Finally, we illustrate and evaluate the effectiveness of the proposed event-triggered algorithms through numerical experiments on simulated.
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