Discovery of implicit relationships from data using linear programming and mixed integer linear programming
Pages 558 - 561
Abstract
Symbolic regression is widely used to discover underlying relationships within observed data in the form of equations. Most studies in the existing literature have focused on explicit equations, i.e., y = f (x). However, in a number of scenarios, such as in complex manifolds and partial differential equations, the relationship among the variables may conform to implicit equation, i.e., f (x) = 0. In this paper, we are interested in uncovering implicit equations that have the form of a general linear model. In particular, we construct improvised formulations of linear programming and mixed-integer linear programming to uncover implicit equations while avoiding trivial solutions. The strengths and limitations of both these approaches are assessed on 23 simulated benchmarks. The results are promising and establish the suitability of the proposed approach for integration with genetic programming frameworks to expedite the search for accurate implicit expressions.
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Index Terms
- Discovery of implicit relationships from data using linear programming and mixed integer linear programming
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Published: 19 July 2022
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