AbstractWe study the regularising properties of Tikhonov regularisation on the sequence space ℓ2 with weighted, non-quadratic penalty term acting separately on the coefficients of a given sequence. We derive sufficient conditions for the penalty term that guarantee the well-posedness of the method, and investigate to which extent the same conditions are also necessary. A particular interest of this paper is the application to the solution of operator equations with sparsity constraints. Assuming a linear growth of the penalty term at zero, we prove the sparsity of all regularised solutions. Moreover, we derive a linear convergence rate under the assumptions of even faster growth at zero and a certain injectivity of the operator to be invert...
Analysis sparsity is a common prior in inverse problem or machine learning including special cases s...
A precise characterization of the extremal points of sublevel sets of nonsmooth penalties provides b...
35 pageIn this paper, we propose an unifying view of several recently proposed structured sparsity-i...
We study the regularising properties of Tikhonov regularisation on the sequence space ℓ2 with weight...
AbstractWe study the regularising properties of Tikhonov regularisation on the sequence space ℓ2 wit...
Sparsity promoting regularization is an important technique for signal reconstruction and several ot...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
International audienceWithin the framework of the l0 regularized least squares problem, we focus, in...
Regularization, or penalization, is a simple yet effective method to promote some desired solution s...
Non-convex sparsity-inducing penalties outperform their convex counterparts, but generally sacrifice...
In this paper we propose a general framework to characterize and solve the optimization problems und...
Iteratively reweighted least square (IRLS) is a popular approach to solve sparsity-enforcing regress...
In this paper, we study linear inverse problems on a closed convex set and the constrained sparsity ...
We study the problem of learning a sparse linear regression vector under additional conditions on th...
As a tractable approach, regularization is frequently adopted in sparse optimization. This gives ris...
Analysis sparsity is a common prior in inverse problem or machine learning including special cases s...
A precise characterization of the extremal points of sublevel sets of nonsmooth penalties provides b...
35 pageIn this paper, we propose an unifying view of several recently proposed structured sparsity-i...
We study the regularising properties of Tikhonov regularisation on the sequence space ℓ2 with weight...
AbstractWe study the regularising properties of Tikhonov regularisation on the sequence space ℓ2 wit...
Sparsity promoting regularization is an important technique for signal reconstruction and several ot...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
International audienceWithin the framework of the l0 regularized least squares problem, we focus, in...
Regularization, or penalization, is a simple yet effective method to promote some desired solution s...
Non-convex sparsity-inducing penalties outperform their convex counterparts, but generally sacrifice...
In this paper we propose a general framework to characterize and solve the optimization problems und...
Iteratively reweighted least square (IRLS) is a popular approach to solve sparsity-enforcing regress...
In this paper, we study linear inverse problems on a closed convex set and the constrained sparsity ...
We study the problem of learning a sparse linear regression vector under additional conditions on th...
As a tractable approach, regularization is frequently adopted in sparse optimization. This gives ris...
Analysis sparsity is a common prior in inverse problem or machine learning including special cases s...
A precise characterization of the extremal points of sublevel sets of nonsmooth penalties provides b...
35 pageIn this paper, we propose an unifying view of several recently proposed structured sparsity-i...