The robust isolated calmness of spectral norm regularized convex matrix optimization problems

This article aims to provide a series of characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for spectral norm regularized convex optimization problems. By establishing the variational properties of the spectral norm function, we directly prove that the KKT mapp...

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Main Authors: Yin Ziran, Chen Xiaoyu, Zhang Jihong
Format: Article
Language:English
Published: De Gruyter 2025-08-01
Series:Open Mathematics
Subjects:
Online Access:https://doi.org/10.1515/math-2025-0189
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author Yin Ziran
Chen Xiaoyu
Zhang Jihong
author_facet Yin Ziran
Chen Xiaoyu
Zhang Jihong
author_sort Yin Ziran
collection DOAJ
description This article aims to provide a series of characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for spectral norm regularized convex optimization problems. By establishing the variational properties of the spectral norm function, we directly prove that the KKT mapping is isolated calm if and only if the strict Robinson constraint qualification (SRCQ) and the second-order sufficient condition (SOSC) hold. Furthermore, we obtain the crucial result that the SRCQ for the primal/dual problem and the SOSC for the dual/primal problem are equivalent. The obtained results can derive more equivalent conditions of the robust isolated calmness of the KKT mapping, thereby enriching the stability theory of spectral norm regularized optimization problems and enhancing the usability of isolated calmness in algorithm applications.
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publisher De Gruyter
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series Open Mathematics
spelling doaj-art-2bd72bae83e8430d84b6e7cc21d2fdd82025-08-20T04:03:17ZengDe GruyterOpen Mathematics2391-54552025-08-0123126327210.1515/math-2025-0189The robust isolated calmness of spectral norm regularized convex matrix optimization problemsYin Ziran0Chen Xiaoyu1Zhang Jihong2School of Science, Dalian Maritime University, Dalian, Liaoning, 116026, P. R. ChinaSchool of Science, Dalian Maritime University, Dalian, Liaoning, 116026, P. R. ChinaSchool of Science, Shenyang Ligong University, Shenyang, Liaoning, 110159, P. R. ChinaThis article aims to provide a series of characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for spectral norm regularized convex optimization problems. By establishing the variational properties of the spectral norm function, we directly prove that the KKT mapping is isolated calm if and only if the strict Robinson constraint qualification (SRCQ) and the second-order sufficient condition (SOSC) hold. Furthermore, we obtain the crucial result that the SRCQ for the primal/dual problem and the SOSC for the dual/primal problem are equivalent. The obtained results can derive more equivalent conditions of the robust isolated calmness of the KKT mapping, thereby enriching the stability theory of spectral norm regularized optimization problems and enhancing the usability of isolated calmness in algorithm applications.https://doi.org/10.1515/math-2025-0189isolated calmnesssecond-order sufficient conditionspectral normstrict robinson constraint qualificationcritical cone90c2590c3165k10
spellingShingle Yin Ziran
Chen Xiaoyu
Zhang Jihong
The robust isolated calmness of spectral norm regularized convex matrix optimization problems
Open Mathematics
isolated calmness
second-order sufficient condition
spectral norm
strict robinson constraint qualification
critical cone
90c25
90c31
65k10
title The robust isolated calmness of spectral norm regularized convex matrix optimization problems
title_full The robust isolated calmness of spectral norm regularized convex matrix optimization problems
title_fullStr The robust isolated calmness of spectral norm regularized convex matrix optimization problems
title_full_unstemmed The robust isolated calmness of spectral norm regularized convex matrix optimization problems
title_short The robust isolated calmness of spectral norm regularized convex matrix optimization problems
title_sort robust isolated calmness of spectral norm regularized convex matrix optimization problems
topic isolated calmness
second-order sufficient condition
spectral norm
strict robinson constraint qualification
critical cone
90c25
90c31
65k10
url https://doi.org/10.1515/math-2025-0189
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