@InProceedings{davis2016sound,
	abstract = {We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence --- among synchronous or asynchronous methods --- on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.},
	archivePrefix = {arXiv},
	arxivId = {1606.02338},
	author = {Davis, D. and Edmunds, B. and Udell, M.},
	eprint = {1606.02338},
	file = {::},
	title = {The Sound of {APALM} Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous {PALM}},
	pdf = {http://arxiv.org/pdf/1606.02338.pdf},
	year = {2016},
	booktitle	= {Advances in Neural Information Processing Systems}
	% author = {Davis, Damek and Edmunds, Brent and Udell, Madeleine},
}