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A COMPLETE CHARACTERIZATION OF THE ROBUST ISOLATED CALMNESS OF NUCLEAR NORM REGULARIZED CONVEX OPTIMIZATION PROBLEMS 被引量:1
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作者 Ying Cui Defeng Sun 《Journal of Computational Mathematics》 SCIE CSCD 2018年第3期441-458,共18页
In this paper, we provide a complete characterization of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for convex constrained optimization problems regularized by the nuclear norm fun... In this paper, we provide a complete characterization of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for convex constrained optimization problems regularized by the nuclear norm function. This study is motivated by the recent work in [8], where the authors show that under the Robinson constraint qualification at a local optimal solution, the KKT solution mapping for a wide class of conic programming problems is robustly isolated calm if and only if both the second order sufficient condition (SOSC) and the strict Robinson constraint qualification (SRCQ) are satisfied. Based on the variational properties of the nuclear norm function and its conjugate, we establish the equivalence between the primal/dual SOSC and the dual/primal SRCQ. The derived results lead to several equivalent characterizations of the robust isolated calmness of the KKT solution mapping and add insights to the existing literature on the stability of nuclear norm regularized convex optimization problems. 展开更多
关键词 Robust isolated calmness Nuclear norm Second order sufficient condition strict robinson constraint qualification
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