  In the era of big data, optimizing large scale machine learning problems
becomes a challenging task and draws significant attention. Asynchronous
optimization algorithms come out as a promising solution. Recently, decoupled
asynchronous proximal stochastic gradient descent (DAP-SGD) is proposed to
minimize a composite function. It is claimed to be able to off-loads the
computation bottleneck from server to workers by allowing workers to evaluate
the proximal operators, therefore, server just need to do element-wise
operations. However, it still suffers from slow convergence rate because of the
variance of stochastic gradient is nonzero. In this paper, we propose a faster
method, decoupled asynchronous proximal stochastic variance reduced gradient
descent method (DAP-SVRG). We prove that our method has linear convergence for
strongly convex problem. Large-scale experiments are also conducted in this
paper, and results demonstrate our theoretical analysis.
