Year: 2022
Type: preprint
Abstract: Generating diverse solutions to the Boolean Satisfiability Problem (SAT) is a hard computational problem with practical applications for testing and functional verification of software and hardware designs. We explore the way to generate such solutions using Denoising Diffusion coupled with a Graph Neural Network to implement the denoising function. We find that the obtained accuracy is similar to the currently best purely neural method and the produced SAT solutions are highly diverse, even if the system is trained with non-random solutions from a standard solver.
Source: arXiv (Cornell University)
Authors: Freivalds, Karlis, Kozlovics, Sergejs
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Nvidia, Latvijas Zinātnes Padome
OpenAlex ID: W4310628640