A Lagrange–Newton algorithm for sparse nonlinear programming

The sparse nonlinear programming (SNP) problem has wide applications in signal and image processing, machine learning and finance, etc. However, the computational challenge posed by SNP has not yet been well resolved due to the nonconvex and discontinuous ℓ 0 -norm involved. In this paper, we resolv...

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Veröffentlicht in:	Mathematical programming Jg. 195; H. 1-2; S. 903 - 928
Hauptverfasser:	Zhao, Chen, Xiu, Naihua, Qi, Houduo, Luo, Ziyan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022 Springer Springer Nature B.V
Schlagworte:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Euler-Lagrange equation Full Length Paper Image processing Iterative methods Lagrangian function Machine learning Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Nonlinear equations Nonlinear programming Numerical Analysis Signal processing Theoretical The Newton method 90C30 90C46 Locally quadratic convergence Sparse nonlinear programming 49M15 Lagrangian equation Application
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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