Parameter Choice by Fast Balancing
Provides a computationally efficient parameter selection method for practitioners in inverse problems, though incremental as it builds on existing balancing principles.
The paper introduces a fast version of the Lepskij balancing principle for choosing the regularization parameter in inverse problems, demonstrating its validity for Tikhonov regularization under both deterministic and stochastic noise with minor solution conditions.
Choosing the regularization parameter for inverse problems is of major importance for the performance of the regularization method. We will introduce a fast version of the Lepskij balancing principle and show that it is a valid parameter choice method for Tikhonov regularization both in a deterministic and a stochastic noise regime as long as minor conditions on the solution are fulfilled.