Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server

Arda Aytekin, KTH Stockholm


This paper presents an asynchronous incremental aggregated gradient
algorithm and its implementation in a parameter server framework for
solving regularized optimization problems. The algorithm can handle
both general convex (possibly non-smooth) regularizers and general
convex constraints. When the empirical data loss is strongly convex, we establish linear convergence rate, give explicit expressions for step-size choices that guarantee convergence to the optimum, and bound the associated convergence factors. The expressions have an explicit dependence on the degree of asynchrony and recover classical results under synchronous operation. Simulations and implementations on commercial compute clouds validate our findings.