A flexible ADMM algorithm for big data applications

DOI10.1007/s10915-016-0306-6zbMath1385.49020arXiv1502.04391OpenAlexW2963403579MaRDI QIDQ1704789

Rachael Tappenden, Daniel P. Robinson

Publication date: 13 March 2018

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1502.04391

zbMATH Keywords

ADMMconvex optimizationregularizationJacobiGauss-Seidelseparable function

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Numerical mathematical programming methods (65K05) Abstract computational complexity for mathematical programming problems (90C60) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Newton-type methods (49M15) Numerical methods based on nonlinear programming (49M37) Complexity and performance of numerical algorithms (65Y20) Implicit function theorems; global Newton methods on manifolds (58C15)

Related Items

First-order algorithms for convex optimization with nonseparable objective and coupled constraints

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