A Semismooth Newton Method for Tikhonov Functionals with Sparsity Constraints

Minimization problems in ${\mathrm{\ell }}^2$ for Tikhonov functionals with sparsity constraints are considered. Sparsity of the solution is ensured by a weighted ${\mathrm{\ell }}^1$ penalty term. The necessary and sufficient condition for optimality is shown to be slantly differentiable (Newton differentiable), hence a semismooth Newton method is applicable. Local superlinear convergence of this method is proved. Numerical examples are provided which show that our method compares favorably with existing approaches.