  Asynchronous parallel optimization algorithms for solving large-scale machine
learning problems have drawn significant attention from academia to industry
recently. This paper proposes a novel algorithm, decoupled asynchronous
proximal stochastic gradient descent (DAP-SGD), to minimize an objective
function that is the composite of the average of multiple empirical losses and
a regularization term. Unlike the traditional asynchronous proximal stochastic
gradient descent (TAP-SGD) in which the master carries much of the computation
load, the proposed algorithm off-loads the majority of computation tasks from
the master to workers, and leaves the master to conduct simple addition
operations. This strategy yields an easy-to-parallelize algorithm, whose
performance is justified by theoretical convergence analyses. To be specific,
DAP-SGD achieves an $O(\log T/T)$ rate when the step-size is diminishing and an
ergodic $O(1/\sqrt{T})$ rate when the step-size is constant, where $T$ is the
number of total iterations.
