A Complete Characterization of the Robust Isolated Calmness of Nuclear Norm Regularized Convex Optimization Problems

A Complete Characterization of the Robust Isolated Calmness of Nuclear Norm Regularized Convex Optimization Problems

Year:    2018

Author:    Ying Cui, Defeng Sun

Journal of Computational Mathematics, Vol. 36 (2018), Iss. 3 : pp. 441–458

Abstract

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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1709-m2017-0034

Journal of Computational Mathematics, Vol. 36 (2018), Iss. 3 : pp. 441–458

Published online:    2018-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    18

Keywords:    Robust isolated calmness Nuclear norm Second order sufficient condition Strict Robinson constraint qualification.

Author Details

Ying Cui

Defeng Sun

  1. On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming

    Cui, Ying

    Sun, Defeng

    Toh, Kim-Chuan

    Mathematical Programming, Vol. 178 (2019), Iss. 1-2 P.381

    https://doi.org/10.1007/s10107-018-1300-6 [Citations: 22]