Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106616
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Liu, R | en_US |
dc.creator | Pan, S | en_US |
dc.creator | Wu, Y | en_US |
dc.creator | Yang, X | en_US |
dc.date.accessioned | 2024-05-17T06:04:40Z | - |
dc.date.available | 2024-05-17T06:04:40Z | - |
dc.identifier.issn | 0926-6003 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106616 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer New York LLC | en_US |
dc.rights | © The Author(s) 2024 | en_US |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
dc.rights | The following publication Liu, R., Pan, S., Wu, Y. et al. An inexact regularized proximal Newton method for nonconvex and nonsmooth optimization. Comput Optim Appl 88, 603–641 (2024) is available at https://doi.org/10.1007/s10589-024-00560-0. | en_US |
dc.subject | Convergence rate | en_US |
dc.subject | Global convergence | en_US |
dc.subject | KL function | en_US |
dc.subject | Metric q-subregularity | en_US |
dc.subject | Nonconvex and nonsmooth optimization | en_US |
dc.subject | Regularized proximal Newton method | en_US |
dc.title | An inexact regularized proximal Newton method for nonconvex and nonsmooth optimization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 603 | en_US |
dc.identifier.epage | 641 | en_US |
dc.identifier.volume | 88 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.1007/s10589-024-00560-0 | en_US |
dcterms.abstract | This paper focuses on the minimization of a sum of a twice continuously differentiable function f and a nonsmooth convex function. An inexact regularized proximal Newton method is proposed by an approximation to the Hessian of f involving the th power of the KKT residual. For = 0, we justify the global convergence of the iterate sequence for the KL objective function and its R-linear convergence rate for the KL objective function of exponent 1/2. For ∈ (0,1), by assuming that cluster points satisfy a locally Hölderian error bound of order q on a second-order stationary point set and a local error bound of order q > 1+ on the common stationary point set, respectively, we establish the global convergence of the iterate sequence and its superlinear convergence rate with order depending on q and . A dual semismooth Newton augmented Lagrangian method is also developed for seeking an inexact minimizer of subproblems. Numerical comparisons with two state-of-the-art methods on 1-regularized Student’s t-regressions, group penalized Student’s t-regressions, and nonconvex image restoration confirm the efficiency of the proposed method. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computational optimization and applications, June 2024, v. 88, no. 2, p. 603-641 | en_US |
dcterms.isPartOf | Computational optimization and applications | en_US |
dcterms.issued | 2024-06 | - |
dc.identifier.scopus | 2-s2.0-85185456349 | - |
dc.identifier.eissn | 1573-2894 | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_TA | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University; National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.TA | Springer Nature (2024) | en_US |
dc.description.oaCategory | TA | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
s10589-024-00560-0.pdf | 811.94 kB | Adobe PDF | View/Open |
Page views
6
Citations as of Jun 30, 2024
Downloads
3
Citations as of Jun 30, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.