neural-combinatorial-optimization

Solve TSP and VRP routing problems faster by applying neural networks for end-to-end learning, which directly outputs high-performance solutions in milliseconds, bypassing classical solver latency.

Yes, you can use neural networks for job shop scheduling and MaxCut optimization. This approach solves complex combinatorial problems by directly learning to output solutions from the data.

Neural networks optimize classical solvers through learning to configure, which adjusts hyperparameters and heuristics, and learning to advise, which provides low-level decisions to improve performance.

Yes, this combinatorial optimization approach requires PyTorch, as it depends on torch, torchvision, and torchtext to execute the necessary neural network training and inference capabilities.

The best way to optimize a transportation route for a fleet in milliseconds is using neural networks, which provide high-performance solutions for vehicle routing problems where classical solvers are too slow.

You should not use neural networks for combinatorial optimization if your task lacks sufficient data for training or requires exact mathematical proofs, as this approach relies on inference speed over guaranteed optimality.