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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Vydané v:	Journal of computational and graphical statistics Ročník 28; číslo 4; s. 821 - 833
Hlavní autori:	Ko, Seyoon, Yu, Donghyeon, Won, Joong-Ho
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.10.2019 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd
Predmet:	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
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