  We resolve the min-max complexity of distributed stochastic convex
optimization (up to a log factor) in the intermittent communication setting,
where $M$ machines work in parallel over the course of $R$ rounds of
communication to optimize the objective, and during each round of
communication, each machine may sequentially compute $K$ stochastic gradient
estimates. We present a novel lower bound with a matching upper bound that
establishes an optimal algorithm.
