Liang Ding

collaborators

4 papers

math.NA2025

$ℓ_{1}^{2} - I^\cdot ℓ_{2}^{2}$ sparsity regularization for nonlinear ill-posed problems

Long Li, Liang Ding

In this study, we investigate the $∥ \cdot ∥_{ℓ_{1}}^{2} - I^\cdot ∥ \cdot ∥_{ℓ_{2}}^{2}$ sparsity regularization with $0 < I^\cdot \leq 1$ , in the context of nonlinea…

math.NA2025

On existence of a variational regularization parameter under Morozov's discrepancy principle

Liang Ding, Long Li, Weimin Han +1

Morozov's discrepancy principle is commonly adopted in Tikhonov regularization for choosing the regularization parameter. Nevertheless, for a general non-linear inverse problem, th…

math.OC2025

SVD method for sparse recovery

Long Li, Liang Ding

Sparsity regularization has garnered significant interest across multiple disciplines, including statistics, imaging, and signal processing. Standard techniques for addressing spar…

math.OC2025

$ℓ_{1}^{2} - I^\cdot ℓ_{2}^{2}$ regularization for sparse recovery

Long Li, Liang Ding

This paper presents a regularization technique incorporating a non-convex and non-smooth term, $ℓ_{1}^{2} - I^\cdot ℓ_{2}^{2}$ , with parameters $0 < I^\cdot \leq 1$ designed to address ill-p…

ℓ12​−I^⋅ℓ22​ sparsity regularization for nonlinear ill-posed problems

On existence of a variational regularization parameter under Morozov's discrepancy principle

SVD method for sparse recovery

ℓ12​−I^⋅ℓ22​ regularization for sparse recovery

$ℓ_{1}^{2} - I^\cdot ℓ_{2}^{2}$ sparsity regularization for nonlinear ill-posed problems

$ℓ_{1}^{2} - I^\cdot ℓ_{2}^{2}$ regularization for sparse recovery