A distributed finite-time optimization algorithm for directed networks of continuous-time agents

This article proposes a new distributed finite-time optimization algorithm for agents under directed graphs. By employing the nonsmooth technique and graph theory, a distributed discontinuous algorithm for continuous-time agents subject to strongly convex local cost functions is first designed with a finite-time distributed estimator, where the gradients of the local cost functions are estimated in finite time. It is shown that for a strongly connected graph and arbitrary initial conditions, the proposed algorithms can achieve consensus, and the systems can converge to the optimal point in finite time. Then, a two-step approach is proposed to achieve finite-time optimization of high-order agents with disturbances under directed graphs. Finally, the validity of the proposed finite-time optimization algorithm is verified by two numerical examples.