Globally Optimal Symbolic Regression
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 2.
6 papers.
- 2023Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbertcited by 18
- 2021AI Descartes: Combining Data and Theory for Derivable Scientific Discoverycited by 8
- 2024In Context Learning and Reasoning for Symbolic Regression with Large Language Modelscited by 7
- 2021Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristicscited by 3
- 2022Learning programs with magic valuescited by 2
- 2024Learning Randomized Reductionscited by 0
- 2024Learning Randomized Reductions24 Dec 2024
- 2024In Context Learning and Reasoning for Symbolic Regression with Large Language Models22 Oct 2024
- 2023Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert18 Aug 2023
- 2022Learning programs with magic values5 Aug 2022
- 2021AI Descartes: Combining Data and Theory for Derivable Scientific Discovery3 Sep 2021
- 2021Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics16 Feb 2021