Distributed Optimization in Prescribed-Time with Time-Varying Cost Functions

In this paper, we study distributed optimization problems with time-varying cost functions, allowing each agent to have a different Hessian matrix. Based on the average consensus estimator and time scale function, a continuous-time timevarying distributed optimization algorithm with prescribed-time convergence is constructed. The convergence of the algorithm is analyzed by Lyapunov theory. Finally, the effectiveness of the algorithm is verified by a practical numerical simulation.