A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares-Reference-Cited by-同舟云学术

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A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares

Published:2024-08-12 Issue:4 Volume:46 Page:A2557-A2581
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Aravkin Aleksandr Y.¹^ORCID,Baraldi Robert²^ORCID,Orban Dominique³

Affiliation:

1. Department of Applied Mathematics, University of Washington, Seattle, WA, USA.

2. Optimization and Uncertainty Quantification, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM, 87125, USA.

3. GERAD and Department of Mathematics and Industrial Engineering, Polytechnique Montréal, QC, Canada.

Funder

Office of Science

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference36 articles.

1. A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization

2. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with Sparsity-Inducing Penalties, Found. Trends Mach. Learn. 4, (2012), pp. 1–106.

3. Julia: A Fresh Approach to Numerical Computing

4. Proximal alternating linearized minimization for nonconvex and nonsmooth problems

5. An inertial forward–backward algorithm for the minimization of the sum of two nonconvex functions

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