Fully Distributed Continuous-Time Algorithm for Nonconvex Optimization Over Unbalanced Digraphs
- Resource Type
- Conference
- Authors
- Zhang, Jin; Hao, Yahui; Liu, Lu; Wang, Xinghu; Ji, Haibo
- Source
- 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) Control, Decision and Information Technologies (CoDIT), 2023 9th International Conference on. :1-6 Jul, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Laplace equations
Cost function
Eigenvalues and eigenfunctions
Information technology
Distributed algorithms
Adaptive control
Multi-agent systems
Fully distributed
nonconvex optimization
continuous-time optimization
unbalanced digraphs
adaptive control
- Language
- ISSN
- 2576-3555
This paper studies the distributed continuous-time nonconvex optimization problem of multi-agent systems over unbalanced digraphs. Each agent is endowed with a local cost function, which is privately known to the agent but not necessarily convex. We aim to drive all the agents to cooperatively converge to the optimal solution of the sum of all local cost functions. Based on the adaptive control approach, a fully distributed algorithm is developed for each agent in the case that neither prior global information concerning network connectivity nor convexity of local cost functions is available. A key feature of the algorithm is that it removes the dependence on the smallest strong convexity constant of local cost functions, and the left eigenvector corresponding to the zero eigenvalue of the Laplacian matrix of unbalanced digraphs.