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
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.
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.
On the use of associative memory in Hopfield networks designed to solve propositional satisfiability problems
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