Machine learning for automated theorem proving: Learning to solve SAT and QSAT
SB Holden - Foundations and Trends® in Machine Learning, 2021 - nowpublishers.com
The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …
archetypal NP-complete problem, and encodings of many problems of practical interest exist …
Machine learning methods in solving the boolean satisfiability problem
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT),
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …
Lime: Learning inductive bias for primitives of mathematical reasoning
While designing inductive bias in neural architectures has been widely studied, we
hypothesize that transformer networks are flexible enough to learn inductive bias from …
hypothesize that transformer networks are flexible enough to learn inductive bias from …
Hardsatgen: Understanding the difficulty of hard sat formula generation and a strong structure-hardness-aware baseline
Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation
approaches can hardly simultaneously capture the global structural properties and maintain …
approaches can hardly simultaneously capture the global structural properties and maintain …
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Propositional satisfiability (SAT) is an NP-complete problem that impacts many research
fields, such as planning, verification, and security. Mainstream modern SAT solvers are …
fields, such as planning, verification, and security. Mainstream modern SAT solvers are …
G4satbench: Benchmarking and advancing sat solving with graph neural networks
Graph neural networks (GNNs) have recently emerged as a promising approach for solving
the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional …
the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional …
Machine Learning for SAT: Restricted Heuristics and New Graph Representations
M Shirokikh, I Shenbin, A Alekseev… - arXiv preprint arXiv …, 2023 - arxiv.org
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications,
including automated planning and scheduling. To solve large instances, SAT solvers have …
including automated planning and scheduling. To solve large instances, SAT solvers have …
Denoising diffusion for sampling sat solutions
K Freivalds, S Kozlovics - arXiv preprint arXiv:2212.00121, 2022 - arxiv.org
Generating diverse solutions to the Boolean Satisfiability Problem (SAT) is a hard
computational problem with practical applications for testing and functional verification of …
computational problem with practical applications for testing and functional verification of …
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Heuristics are crucial in SAT solvers, while no heuristic rules are suitable for all problem
instances. Therefore, it typically requires to refine specific solvers for specific problem …
instances. Therefore, it typically requires to refine specific solvers for specific problem …
Realtime gray-box algorithm configuration using cost-sensitive classification
D Weiss, K Tierney - Annals of Mathematics and Artificial Intelligence, 2023 - Springer
A solver's runtime and the quality of the solutions it generates are strongly influenced by its
parameter settings. Finding good parameter configurations is a formidable challenge, even …
parameter settings. Finding good parameter configurations is a formidable challenge, even …