1 research outputs found
RLC Circuits based Distributed Mirror Descent Method
We consider distributed optimization with smooth convex objective functions
defined on an undirected connected graph. Inspired by mirror descent mehod and
RLC circuits, we propose a novel distributed mirror descent method. Compared
with mirror-prox method, our algorithm achieves the same
iteration complexity with only half the computation cost per iteration. We
further extend our results to cases where a) gradients are corrupted by
stochastic noise, and b) objective function is composed of both smooth and
non-smooth terms. We demonstrate our theoretical results via numerical
experiments