Continuous-time online distributed constrained optimization via unbalanced digraphs

In this paper, online distributed constrained optimization is investigated by employing a continuous-time multi-agent systems. The objective of the agents is to cooperatively minimize the sum of time-varying cost functions subject to a convex set at each time. Each agent can only have access to its...

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Vydáno v:IEEE International Conference on Control and Automation (Print) s. 807 - 813
Hlavní autoři: Lu, Kaihong, Xu, Hang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 27.06.2022
Témata:
Automation
Cost function
Heuristic algorithms
Multi-agent systems
Trajectory
ISSN:1948-3457
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Abstract In this paper, online distributed constrained optimization is investigated by employing a continuous-time multi-agent systems. The objective of the agents is to cooperatively minimize the sum of time-varying cost functions subject to a convex set at each time. Each agent can only have access to its own cost function and the convex set, and cost function in the future is not available. To address this problem, we propose a modified online distributed "projection+gradient" algorithm, which involves each agent minimizing its own cost function while exchanging local state information with others via an unbalanced digraph. Performance of the algorithm is measured by dynamic regrets. Under mild assumptions on the graph, we prove that if the rate of a minimizer's variation is within a certain range, then regrets, as well as the violation of constraint, grow sublinearly. A simulation is presented to demonstrate the effectiveness of our theoretical results.
AbstractList In this paper, online distributed constrained optimization is investigated by employing a continuous-time multi-agent systems. The objective of the agents is to cooperatively minimize the sum of time-varying cost functions subject to a convex set at each time. Each agent can only have access to its own cost function and the convex set, and cost function in the future is not available. To address this problem, we propose a modified online distributed "projection+gradient" algorithm, which involves each agent minimizing its own cost function while exchanging local state information with others via an unbalanced digraph. Performance of the algorithm is measured by dynamic regrets. Under mild assumptions on the graph, we prove that if the rate of a minimizer's variation is within a certain range, then regrets, as well as the violation of constraint, grow sublinearly. A simulation is presented to demonstrate the effectiveness of our theoretical results.
Author Xu, Hang
Lu, Kaihong
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Snippet In this paper, online distributed constrained optimization is investigated by employing a continuous-time multi-agent systems. The objective of the agents is...
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SubjectTerms Automation
Cost function
Heuristic algorithms
Multi-agent systems
Trajectory
Title Continuous-time online distributed constrained optimization via unbalanced digraphs
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