Abstract
This paper discusses a distributed time-varying convex optimization problem that agents have different Hessian matrices with inequality constraints. The objective is to minimize the sum of local time-varying objective functions of agents constrained by time-varying inequalities. Under the condition of undirected connected graph, a distributed continuous time consistency algorithm is designed based on average consensus estimator, sign function and log-barrier penalty function. The main idea of the algorithm proposed in this paper is to use the estimator to estimate the global information, make the state of agents reach consensus and achieve gradient descent to track the optimal solution. Theoretical findings show that all agents can reach an agreement, the proposed algorithm can track the optimal solution of the time-varying optimization problem. The effectiveness of the theoretical results is verified through a numerical examples.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
