Graph Neural Networks – Optimization Online

Learning-Augmented Combinatorial Solvers: A Solver-Architecture Taxonomy

Published: 2025/11/09, Updated: 2026/10/05

Applications - OR and Management Sciences, Combinatorial Optimization, Optimization in Data Science Boolean Satisfiability, Conflict-Driven Clause Learning, Constraint Satisfaction Problem, Graph Neural Networks, Hybrid Solvers, Lazy Clause Generation, Learned Heuristics, machine learning, Neuro-Symbolic Optimization, reinforcement learning, Transformer Models

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

COIL: A Deep Architecture for Column Generation

Published: 2022/09/06

Linear Programming column generation, Graph Neural Networks, Imitation Learning, stabilization

Column generation is a popular method to solve large-scale linear programs with an exponential number of variables. Several important applications, such as the vehicle routing problem, rely on this technique in order to be solved. However, in practice, column generation methods suffer from slow convergence (i.e. they require too many iterations). Stabilization techniques, which carefully … Read more