Distributed Randomized Gradient-Free Mirror Descent Algorithm for Constrained Optimization
Zhan Yu, Daniel W. C. Ho, Deming Yuan
Abstract
This article is concerned with the multiagent optimization problem. A distributed randomized gradient-free mirror descent (DRGFMD) method is developed by introducing a randomized gradient-free oracle in the mirror descent scheme where the non-Euclidean Bregman divergence is used. The classical gradient descent method is generalized without using subgradient information of objective functions. The proposed algorithms are the first distributed non-Euclidean zeroth-order methods, which achieve an approximate <inline-formula><tex-math notation="LaTeX">$O(\frac{1}{\sqrt{T}})$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula>-rate of convergence, recovering the best known optimal rate of distributed nonsmooth constrained convex optimization. Moreover, a decentralized reciprocal weighted averaging (RWA) approximating sequence is first investigated, the convergence for RWA sequence is shown to hold over time-varying graph. Rates of convergence are comprehensively explored for the algorithm with RWA (DRGFMD-RWA). The technique on constructing the decentralized RWA sequence provides new insight in searching for minimizers in distributed algorithms.