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Auteur principal: Chaudhary, Vivek
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2404.14457
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author Chaudhary, Vivek
author_facet Chaudhary, Vivek
contents Graph coloring is a problem with varied applications in industry and science such as scheduling, resource allocation, and circuit design. The purpose of this paper is to establish if a new gradient based iterative solver framework known as heat diffusion can solve the graph coloring problem. We propose a solution to the graph coloring problem using the heat diffusion framework. We compare the solutions against popular methods and establish the competitiveness of heat diffusion method for the graph coloring problem.
format Preprint
id arxiv_https___arxiv_org_abs_2404_14457
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Graph Coloring Using Heat Diffusion
Chaudhary, Vivek
Machine Learning
05
Graph coloring is a problem with varied applications in industry and science such as scheduling, resource allocation, and circuit design. The purpose of this paper is to establish if a new gradient based iterative solver framework known as heat diffusion can solve the graph coloring problem. We propose a solution to the graph coloring problem using the heat diffusion framework. We compare the solutions against popular methods and establish the competitiveness of heat diffusion method for the graph coloring problem.
title Graph Coloring Using Heat Diffusion
topic Machine Learning
05
url https://arxiv.org/abs/2404.14457