This letter presents an almost sure convergence of the zeroth-order mirror descent (ZOMD) algorithm. The algorithm admits non-smooth convex functions and a biased oracle which only provides noisy function value at any desired point. We approximate subgradient objective using Nesterov’s Gaussian Approximation (NGA) with certain alternations suggested by some practical applications. prove iterate...

In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex convex optimization, with a focus on addressing constrained high-dimensional setting, saddle point avoiding. To handle first generalizations of the conditional gradient algorithm achieving rates similar to standard using only information. facilitate optimization in high dimensions, explore advan...