Alternating Minimization for Regression with Tropical Rational Functions

Fuente: arXiv
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Main Authors: Dunbar, Alex, Ruthotto, Lars
Format: Preprint
Published: 2023
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author Dunbar, Alex
Ruthotto, Lars
author_facet Dunbar, Alex
Ruthotto, Lars
contents We propose an alternating minimization heuristic for regression over the space of tropical rational functions with fixed exponents. The method alternates between fitting the numerator and denominator terms via tropical polynomial regression, which is known to admit a closed form solution. We demonstrate the behavior of the alternating minimization method experimentally. Experiments demonstrate that the heuristic provides a reasonable approximation of the input data. Our work is motivated by applications to ReLU neural networks, a popular class of network architectures in the machine learning community which are closely related to tropical rational functions.
format Preprint
id arxiv_https___arxiv_org_abs_2305_20072
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Alternating Minimization for Regression with Tropical Rational Functions
Dunbar, Alex
Ruthotto, Lars
Optimization and Control
Machine Learning
90C24, 14T90, 62J02
We propose an alternating minimization heuristic for regression over the space of tropical rational functions with fixed exponents. The method alternates between fitting the numerator and denominator terms via tropical polynomial regression, which is known to admit a closed form solution. We demonstrate the behavior of the alternating minimization method experimentally. Experiments demonstrate that the heuristic provides a reasonable approximation of the input data. Our work is motivated by applications to ReLU neural networks, a popular class of network architectures in the machine learning community which are closely related to tropical rational functions.
title Alternating Minimization for Regression with Tropical Rational Functions
topic Optimization and Control
Machine Learning
90C24, 14T90, 62J02
url https://arxiv.org/abs/2305.20072