Asynchronous stochastic convex optimization
This work addresses the challenge of efficient parallel and asynchronous optimization for machine learning practitioners, though it appears incremental as it extends existing stochastic gradient methods to asynchronous contexts.
The paper tackles the problem of asynchronous stochastic gradient procedures for convex optimization, showing that they achieve optimal convergence rates asymptotically, comparable to standard stochastic gradient methods, with empirical evidence supporting their strong performance in parallel and asynchronous settings.
We show that asymptotically, completely asynchronous stochastic gradient procedures achieve optimal (even to constant factors) convergence rates for the solution of convex optimization problems under nearly the same conditions required for asymptotic optimality of standard stochastic gradient procedures. Roughly, the noise inherent to the stochastic approximation scheme dominates any noise from asynchrony. We also give empirical evidence demonstrating the strong performance of asynchronous, parallel stochastic optimization schemes, demonstrating that the robustness inherent to stochastic approximation problems allows substantially faster parallel and asynchronous solution methods.