Least Squares Auto-Tuning
This addresses a common issue in computational methods for data fitting, but it appears incremental as it builds on standard least squares.
The paper tackles the discrepancy between the least squares objective and the true objective in data fitting by introducing a parametrized least squares problem with automatic parameter adjustment, resulting in a method called least squares auto-tuning.
Least squares is by far the simplest and most commonly applied computational method in many fields. In almost all applications, the least squares objective is rarely the true objective. We account for this discrepancy by parametrizing the least squares problem and automatically adjusting these parameters using an optimization algorithm. We apply our method, which we call least squares auto-tuning, to data fitting.