## An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

*Ji Liu, Stephen J. Wright, Christopher Ré, Victor Bittorf, Srikrishna Sridhar*; 16(Feb):285−322, 2015.

### Abstract

We describe an asynchronous parallel stochastic coordinate
descent algorithm for minimizing smooth unconstrained or
separably constrained functions. The method achieves a linear
convergence rate on functions that satisfy an essential strong
convexity property and a sublinear rate ($1/K$) on general
convex functions. Near-linear speedup on a multicore system can
be expected if the number of processors is $O(n^{1/2})$ in
unconstrained optimization and $O(n^{1/4})$ in the separable-
constrained case, where $n$ is the number of variables. We
describe results from implementation on 40-core processors.

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