Multi-parameter Tikhonov regularization for linear ill-posed operator equations

We consider solving linear ill-posed operator equations. Based on a multi-scale decomposition for the solution space, we propose a multi-parameter regularization for solving the equations. We establish weak and strong convergence theorems for the multi-parameter regularization solution. In particular, based on the eigenfunction decomposition, we develop a posteriori choice strategy for multi-parameters which gives a regularization solution with the optimal error bound. Several practical choices of multi-parameters axe proposed. We also present numerical experiments to demonstrate the outperformance of the multiparameter regularization over the single parameter regularization.