43 citations · 80 across the 16 of their papers we have counts for
16 papers
Stability results for regularized least-squares problems via generalized Hessian expressions and monotone generalized equations
Leo Smulansky, Tim Hoheisel, Tran T. A. Nghia
We study perturbation and stability properties of solution mappings associated with convex regularized least-squares problems. We first establish an implicit function theorem for g…
Isolated Calmness in Regularized Convex Optimization
Tran T. A. Nghia, Huy N. Pham
This paper studies the isolated calmness of the optimal solution mapping and the associated Lagrange system for regularized convex composite optimization problems. Several necessar…
Nonsmooth Newton methods with effective subspaces for polyhedral regularization
Tran T. A. Nghia, Nghia V. Vo, Khoa V. H. Vu
We propose several new nonsmooth Newton methods for solving convex composite optimization problems with polyhedral regularizers, while avoiding the computation of complicated secon…
Stable Recovery of Regularized Linear Inverse Problems
Tran T. A. Nghia, Huy N. Pham, Nghia V. Vo
Recovering a low-complexity signal from its noisy observations by regularization methods is a cornerstone of inverse problems and compressed sensing. Stable recovery ensures that t…
Lipschitz stability of least-squares problems regularized by functions with -cone reducible conjugates
Ying Cui, Tim Hoheisel, Tran T. A. Nghia +1
In this paper, we study Lipschitz continuity of the solution mappings of regularized least-squares problems for which the convex regularizers have (Fenchel) conjugates that are $\m…
Sharp, strong and unique minimizers for low complexity robust recovery
Jalal Fadili, Tran T. A. Nghia, Trinh T. T. Tran
In this paper, we show the important roles of sharp minima and strong minima for robust recovery. We also obtain several characterizations of sharp minima for convex regularized op…