Large-scale regression with non-convex loss and penalty

DOI10.1016/j.apnum.2020.07.006zbMath1445.62161OpenAlexW3044556875MaRDI QIDQ2192647

Andrea Martinelli, Omar De la Cruz Cabrera, Marco Donatelli, Alessandro Buccini, Lothar Reichel

Publication date: 17 August 2020

Published in: Applied Numerical Mathematics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11584/296257

zbMATH Keywords

robustnessregularizationnon-convex optimizationregression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35) Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (8)

Limited memory restarted \(\ell^p\)-\(\ell^q\) minimization methods using generalized Krylov subspaces ⋮ Variable selection in saturated and supersaturated designs via lp-lq minimization ⋮ A reduced half thresholding algorithm ⋮ On the choice of regularization matrix for an \(\ell_2\)-\(\ell_q\) minimization method for image restoration ⋮ Fast alternating direction multipliers method by generalized Krylov subspaces ⋮ A comparison of parameter choice rules for \(\ell^p\)-\(\ell^q\) minimization ⋮ Krylov subspace split Bregman methods ⋮ A variational non-linear constrained model for the inversion of FDEM data*

Uses Software

• alr3
• sn
• alr4
• ElemStatLearn
• sparsenet

Cites Work

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