Efficient Sparse Semismooth Newton Methods for the Clustered Lasso Problem-Reference-Cited by-同舟云学术

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Efficient Sparse Semismooth Newton Methods for the Clustered Lasso Problem

Published:2019-01 Issue:3 Volume:29 Page:2026-2052
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Lin Meixia,Liu Yong-Jin,Sun Defeng,Toh Kim-Chuan

Funder

Hong Kong Polytechnic University

Ministry of Education - Singapore

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

https://epubs.siam.org/doi/pdf/10.1137/18M1207752

Reference35 articles.

1. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

2. Active set algorithms for isotonic regression; A unifying framework

3. Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR

5. An efficient inexact symmetric Gauss–Seidel based majorized ADMM for high-dimensional convex composite conic programming

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1. Minimum residual shift-splitting iteration method for non-Hermitian positive definite and positive semidefinite linear systems;Applied Mathematics Letters;2025-01

2. Efficient path algorithms for clustered Lasso and OSCAR;Japanese Journal of Statistics and Data Science;2024-09-14

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4. Generalized Newton Method with Positive Definite Regularization for Nonsmooth Optimization Problems with Nonisolated Solutions;Journal of Optimization Theory and Applications;2024-03-13

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