BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence

Subjects: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC)

[…] We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than current state of the art models. Key features of the proposed framework are: i) it accommodates inconsistent read, meaning that components of the vector variables may be written by some cores while being simultaneously read by others; ii) it covers in a unified way several different specific solution methods, and iii) it accommodates a variety of possible parallel computing architectures. […]

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By Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Source: arxiv.org