Distributed Optimization Design of Multi-Agent Systems With Nonidentical Packet Losses

This paper considers the distributed optimization problem of multi-agent systems (MASs) with nonidentical pack-et losses. A sampled-data-based distributed optimization algorithm is proposed such that the states of all agents converge in probability to the optimal solution of the sum of the objective functions. Compared with the existing relevant works, the packet losses are not required to be identical, that is, all the communication channels of MAS are not required to be blocked and linked simultaneously.