A generalized algorithm for continuous-time distributed optimization

This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.