  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.
