# Globally Optimal Symbolic Regression

arXiv: https://arxiv.org/abs/1710.10720 · HTML: https://mlsanity.com/paper/1710.10720/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2017.

## Built on by (most cited first, 6 of 6)

- [Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert](https://mlsanity.com/paper/2308.09474/) (2023)
- [AI Descartes: Combining Data and Theory for Derivable Scientific Discovery](https://mlsanity.com/paper/2109.01634/) (2021)
- [In Context Learning and Reasoning for Symbolic Regression with Large Language Models](https://mlsanity.com/paper/2410.17448/) (2024)
- [Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics](https://mlsanity.com/paper/2102.08351/) (2021)
- [Learning programs with magic values](https://mlsanity.com/paper/2208.03238/) (2022)
- [Learning Randomized Reductions](https://mlsanity.com/paper/2412.18134/) (2024)

## Abstract (arXiv)

In this study we introduce a new technique for symbolic regression that guarantees global optimality. This is achieved by formulating a mixed integer non-linear program (MINLP) whose solution is a symbolic mathematical expression of minimum complexity that explains the observations. We demonstrate our approach by rediscovering Kepler's law on planetary motion using exoplanet data and Galileo's pendulum periodicity equation using experimental data.