ML time predictor combined with simulated annealing optimizes variable orderings to accelerate Boolean Characteristic Set solving of equation systems, with probabilistic complexity bounds derived.
A new efficient algorithm for computing gröbner bases without reduction to zero(f5).proc issac, 139(1-3):75–83
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Variables Ordering Optimization in Boolean Characteristic Set Method Using Simulated Annealing and Machine Learning-based Time Prediction
ML time predictor combined with simulated annealing optimizes variable orderings to accelerate Boolean Characteristic Set solving of equation systems, with probabilistic complexity bounds derived.