Method and system for hyperparameter and algorithm selection for mixed integer linear programming problems using representation learning

This invention describes a system that helps choose the best way to solve complex optimization problems, known as Mixed Integer Linear Programming (MILP). It does this by converting each problem into a graph, extracting key information, and then using machine learning to learn a unique digital fingerprint for it. A trained model then uses this fingerprint to recommend the most effective solver, specifically using bipartite graphs and one-hop statistics for feature generation.

Why it matters: Filed before the widespread adoption and maturity of Graph Neural Networks (GNNs) and advanced representation learning techniques. The tools and computational power for building such a system are significantly more accessible and powerful now.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedHeavy software system, machine learning, graph processing

3 AI reinvention directions for this patent

See all three

AI gives you a few directions you could take this. Pick one, and we check whether your version is different enough to patent, then write the filing.

Reinvent this with AI

Where this one came from

Every morning we go through millions of abandoned patents and pick out 15 worth your time. This was one of today’s.

This one

+10