Strong calmness of perturbed KKT system for a class of conic programming with degenerate solutions. (3rd June 2019)

• Record Type: Journal Article
• Authors: Liu, Y. L.; Pan, S. H.
• Is Part Of: Optimization. Volume 68:Number 6(2019)
• Page Start: 1131
• Page End: 1156
• Publication Date: 2019-06-03
• Subjects: Conic programming -- KKT solution mapping -- strong calmness -- local error bound -- pseudo-isolated calmness -- noncritical multiplier; 49K40 -- 90C31 -- 49J53; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2019.1576666
• Languages: English
• ISSNs: 0233-1934

Abstract

ABSTRACT: This paper is concerned with the strong calmness of the KKT solution mapping for a class of canonically perturbed conic programming, which plays a central role in achieving fast convergence under situations when the Lagrange multiplier associated to a solution of these conic optimization problems is not unique. We show that the strong calmness of the KKT solution mapping is equivalent to a local error bound for the solutions to the perturbed KKT system, and is also equivalent to the pseudo-isolated calmness of the stationary point mapping along with the calmness of the multiplier set mapping at the corresponding reference point. Sufficient conditions are also provided for the strong calmness by establishing the pseudo-isolated calmness of the stationary point mapping in terms of the noncriticality of the associated multiplier, and the calmness of the multiplier set mapping in terms of a relative interior condition for the multiplier set. These results cover and extend the existing ones in Hager and Gowda [Stability in the presence of degeneracy and error estimation. Math Program. 1999;85:181–192]; Izmailov and Solodov [Stabilized SQP revisited. Math Program. 2012;133:93–120] for nonlinear programming and in Cui et al. [On the asymptotic superlinear convergence of the augmented Lagrangian method for semidefinite programming with multiple solutions. 2016, arXiv: 1610.00875v1]; Zhang and Zhang [Critical multipliers in semidefinite programming. 2018, arXiv: 1801.02218v1] for semidefinite programming.