Smoothing composite proximal gradient algorithm for sparse group Lasso problems with nonsmooth loss functions

In recent years, the sparse and group sparse optimization problem has attracted extensive attention due to its wide applications in statistics, bioinformatics, signal interpretation and machine learning, which yields the sparsity both in group-wise and element-wise. In this paper, the sparse and gro...

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Vydané v:	Journal of applied mathematics & computing Ročník 70; číslo 3; s. 1887 - 1913
Hlavní autori:	Shen, Huiling, Peng, Dingtao, Zhang, Xian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024 Springer Nature B.V
Predmet:	Algorithms Approximation Bioinformatics Computational Mathematics and Numerical Analysis Gene expression Machine learning Mathematical and Computational Engineering Mathematics Mathematics and Statistics Mathematics of Computing Optimization Original Research Smoothing Sparsity Theory of Computation Smoothing composite proximal gradient algorithm Anti-outlier 65K06 Nonsmooth loss function 90C30 90C06 Sublinear convergence rate Sparse group Lasso problem
ISSN:	1598-5865, 1865-2085
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