ilploss: fast solver-free training of neural-ILP architectures

ilploss is a pytorch-based library which lets you train models with an Integer Linear Programming (ILP) output layer, without ever calling an ILP solver during training. It implements techniques from the paper A Solver-Free Framework for Scalable Learning in Neural ILP Architectures, accepted at NeurIPS 2022. Full code for the results in the paper can be found here.

Install

In a python environment with torch and gurobi (not gurobipy) installed:

pip install git+https://github.com/rishabh-ranjan/ilploss

Usage

The ilploss library provides the following modules:

1. ilploss.encoders: get ILP from input
2. ilploss.samplers: sample negatives
3. ilploss.losses: compute and balance loss terms
4. ilploss.solvers: solve batched ILPs at inference, in parallel

ILP instances are specified by a, b, c under the convention that for solution vector z, the cost c^T z is to be minimized under the constraints a @ z + b >= 0 (@ denotes matrix multiply).

Check out the source code for further details.

Example

A demo script is provided here. This replicates the ILP-Loss experiments on random constraints from Table 2 of the paper. To run the demo:

git clone https://github.com/rishabh-ranjan/ilploss
cd ilploss
tests/random.py tests/data/random/dense/16_dim/8_const/0/dataset.pt

You can choose any file from tests/data as the argument.

Citation

@inproceedings{ilploss,
  author = {Nandwani, Yatin and Ranjan, Rishabh and Mausam and Singla, Parag},
  title = {A Solver-Free Framework for Scalable Learning in Neural ILP Architectures},
  booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, November 29-Decemer 1, 2022},
  year = {2022},
}