# Least Squares Auto-Tuning

## TL;DR

This paper introduces least squares auto-tuning, a method that parametrizes and automatically adjusts the least squares objective to better match true objectives in data fitting applications.

## Contribution

It proposes a novel auto-tuning approach for least squares problems by parametrizing and optimizing the objective function.

## Key findings

- Improved data fitting accuracy
- Automatic parameter adjustment enhances model performance
- Applicable to various least squares applications

## Abstract

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.