Sorathan Chaturapruek

Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care

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 proced…