7 citations · 21 across the 16 of their papers we have counts for
4 papers · 1 filter
On the R-superlinear convergence of the KKT residues generated by the augmented Lagrangian method for convex composite conic programming
Ying Cui, Defeng Sun, Kim-Chuan Toh
Due to the possible lack of primal-dual-type error bounds, the superlinear convergence for the Karush-Kuhn-Tucker (KKT) residues of the sequence generated by augmented Lagrangian m…
On efficiently solving the subproblems of a level-set method for fused lasso problems
Xudong Li, Defeng Sun, Kim-Chuan Toh
In applying the level-set method developed in [Van den Berg and Friedlander, SIAM J. on Scientific Computing, 31 (2008), pp.~890--912 and SIAM J. on Optimization, 21 (2011), pp.~12…
A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery
Shujun Bi, Shaohua Pan, Defeng Sun
This paper concerns with a noisy structured low-rank matrix recovery problem which can be modeled as a structured rank minimization problem. We reformulate this problem as a mathem…
A complete characterization on the robust isolated calmness of the nuclear norm regularized convex optimization problems
Ying Cui, Defeng Sun
In this paper, we provide a complete characterization on the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for convex constrained optimization problems…