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arXiv · 1710.10720

Globally Optimal Symbolic Regression

Vernon Austel·Sanjeeb Dash·Oktay Gunluk·Lior Horesh·Leo Liberti·Giacomo Nannicini·Baruch Schieber
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Abstract

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.

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Vernon Austel, Sanjeeb Dash, Oktay Gunluk, Lior Horesh, Leo Liberti, Giacomo Nannicini, Baruch Schieber. 2017-11-15. Globally Optimal Symbolic Regression. https://arxiv.org/abs/1710.10720

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