Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration

Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of nonsmooth and linear functions. Examples include regression under structured sparsity assumptions. Popular algorithms for solving such problems, for example, ADMM, often i...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 28; H. 4; S. 821 - 833
Hauptverfasser:	Ko, Seyoon, Yu, Donghyeon, Won, Joong-Ho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 02.10.2019 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd
Schlagworte:	Acceleration Advances in Sampling and Optimization Algorithms Computer networks Convergence Distributed computing Distributed processing GPU Linear functions Machine learning Monotone operator theory Nonsmooth optimization Operator splitting Optimization Parallel processing Regression analysis Sparsity Statistical analysis
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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