Distributed Stochastic Mirror Descent Algorithm Over Time-varying Network
Yinghui Wang, Hongbing Zhou, Yiguang Hong · 2018
In this paper, we propose a distributed stochastic mirror descent algorithm for solving distributed general (nondifferentiable) convex optimization problem over a time-varying multi-agent network. We adopt Bregman divergence rather than Euclidean distance as the augmented distance measuring function to solve the distributed first-order Lagrangian-based convex optimization problem. With a fixed step-size, our algorithm achieves a convergence rate O(T ) with an error bound, which is the best known convergence rate for distributed first-order algorithms. Numerical experiments demonstrate the performance of the proposed algorithm.