Practical Prescribed-Time Distributed Nonconvex Optimization via a Time-Base Generator
Qinlong Lin, Yang Liu, Jianquan Lu, Weihua Gui · SIAM Journal on Control and Optimization · 2025
Abstract. In this paper, we propose two novel multiagent systems for the distributed optimization problems. Different from the existing distributed optimal approaches, we propose the new time-base generators (TBGs) for practical prescribed-time nonconvex optimization. Leveraging the proposed TBGs, we study the robustness and boundedness of the Lyapunov function. We prove that our approach achieves practical prescribed-time convergence to the optimal solution if the cost functions exhibit non-strongly convex or even nonconvex characteristics. Furthermore, we prove that our approaches converge to the optimal solution if cost functions are generalized smooth, and exhibit faster convergence rate and CPU efficiency. Finally, we present numerous numerical simulation examples to confirm the effectiveness of our approaches.