An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems

The sparse group Lasso is a widely used statistical model which encourages the sparsity both on a group and within the group level. In this paper, we develop an efficient augmented Lagrangian method for large-scale non-overlapping sparse group Lasso problems with each subproblem being solved by a su...

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Veröffentlicht in:	Mathematical programming Jg. 179; H. 1-2; S. 223 - 263
Hauptverfasser:	Zhang, Yangjing, Zhang, Ning, Sun, Defeng, Toh, Kim-Chuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2020 Springer Nature B.V
Schlagworte:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Convergence Full Length Paper Iterative methods Lagrange multiplier Mapping Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Newton methods Nonlinear programming Numerical Analysis Robustness (mathematics) Sparsity Statistical models Theoretical Augmented Lagrangian method 62J05 Semismooth Newton method 90C25 Sparse group Lasso 90C06 Generalized Jacobian
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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