Q-curve and area rules for choosing heuristic parameter in Tikhonov regularization
For practitioners of inverse problems, this provides a heuristic parameter choice rule that is more reliable than existing heuristics and competitive with methods requiring known noise level.
The paper addresses the problem of choosing the regularization parameter in Tikhonov method when noise level is unknown. It proposes Q-curve and area rules that select a local minimizer of the quasi-optimality function, achieving results comparable to the discrepancy principle using exact noise level, and outperforming previous heuristic rules in numerical experiments.
We consider choice of the regularization parameter in Tikhonov method if the noise level of the data is unknown. One of the best rules for the heuristic parameter choice is the quasi-optimality criterion where the parameter is chosen as the global minimizer of the quasi-optimality function. In some problems this rule fails. We prove that one of the local minimizers of the quasi-optimality function is always a good regularization parameter. For choice of the proper local minimizer we propose to construct the Q-curve which is the analogue of the L-curve, but on x-axis we use modified discrepancy instead of discrepancy and on the y-axis the quasi-optimality function instead of the norm of the approximate solution. In area rule we choose for the regularization parameter such local minimizer of the quasi-optimality function for which the area of polygon, connecting on Q-curve this minimum point with certain maximum points, is maximal. We also provide a posteriori error estimates of the approximate solution, which allows to check the reliability of parameter chosen heuristically. Numerical experiments on extensive set of test problems confirm that the proposed rules give much better results than previous heuristic rules. Results of proposed rules are comparable with results of the discrepancy principle and the monotone error rule, if last two rules use the exact noise level.