Learning-Augmented Combinatorial Solvers: A Solver-Architecture Taxonomy
This paper presents a solver-architecture taxonomy of machine-learning and reinforcement-learning interventions in combinatorial solving. Rather than treating learning-augmented solving as a collection of isolated applications, we organize existing methods according to the solver-level decisions they influence, including propagation, branching, conflict analysis, restart control, structural analysis, and formulation-level reasoning. The discussion focuses on SAT/CDCL, Lazy Clause … Read more