Partially observed distributed optimization under unknown-but-bounded disturbances
In this paper, we consider non-stationary distributed optimization with partially observed parameters with acceleration based on the estimate sequence proposed by Y. Nesterov. We formulate this partial observability as time-varying communication matrix defined for each parameter separately. We propose the new distributed algorithm combining the accelerated Simultaneous Perturbation Stochastic Approximation (SPSA) and the described communication scheme as well as show its theoretical properties. The simulation validates the proposed algorithm in multi-sensor multi-target tracking problem over delayed channels.