DeepAI AI Chat
Log In Sign Up

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics

02/16/2021
by   Jongeun Kim, et al.
0

In this paper we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. The symbolic regression problem can be formulated as a nonconvex mixed-integer nonlinear program (MINLP), where binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program-based method and a neural-network-based method from the literature.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/11/2020

Symbolic Regression using Mixed-Integer Nonlinear Optimization

The Symbolic Regression (SR) problem, where the goal is to find a regres...
10/29/2017

Globally Optimal Symbolic Regression

In this study we introduce a new technique for symbolic regression that ...
04/24/2020

Simulating and Evaluating Rebalancing Strategies for Dockless Bike-Sharing Systems

Following the growth of dock-based bike sharing systems as an eco-friend...
12/14/2022

Network Coding: An Optimization Approach

We consider the problem of computing the capacity of a coded, multicast ...
09/27/2018

Packing of Circles on Square Flat Torus as Global Optimization of Mixed Integer Nonlinear problem

The article demonstrates rather general approach to problems of discrete...
03/13/2015

Fuzzy Mixed Integer Optimization Model for Regression Approach

Mixed Integer Optimization has been a topic of active research in past d...