SAT solvers' running time prediction using graph neural networks
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Updated
Aug 9, 2022 - Python
SAT solvers' running time prediction using graph neural networks
This repository gathers the material of my course conclusion work (TCC) at Unisinos - theme of the work: Quantum Computing - Analysis of Quantum Algorithms for Solving the SAT Problem in the NISQ Era
Gap puzzle generator and solver using C++ and Cadical
Reduced NP‑Hard problems such as K‑Colorability, K‑clique, Maximum clique to SAT problem using Weighted Partial Max‑SAT Input format,created using boolean formulas, in order to find a satisfying interpretation. Families are represented as vertices of a graph.
This is a simple planning problem solver that encodes a bounded planning problem as propositional logic and uses https://fmv.jku.at/limboole/ to solve it. I did this for my formal models class.
On the use of associative memory in Hopfield networks designed to solve propositional satisfiability problems
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