Hybrid Mixed Integer Programming with Machine Learning for Digital Asset Management

Abstract

Solving Mixed Integer Programming (MIP) problems is critical for numerous applications in digital asset management. Traditional solvers, which rely on techniques like branch-and-bound and branch-and-cut, face computational challenges when dealing with large-scale and complex problem instances. Recent advances in machine learning (ML) have introduced new approaches to enhance these traditional solvers by optimizing key decision-making processes such as branching, cut generation, and heuristic selection. In this paper, we examine hybrid methods that combine ML with classical MIP methodologies, leveraging supervised learning, reinforcement learning, and graph neural networks to improve solver efficiency and scalability. These hybrid methods significantly reduce computational overhead by making smarter decisions during the optimization process, resulting in faster convergence and higher-quality solutions. We also discuss the challenges of generalization and integration of ML models with existing solvers and propose future research directions to further advance this field.