# neural_lns

**Repository Path**: mirrors_deepmind/neural_lns

## Basic Information

- **Project Name**: neural_lns
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-22
- **Last Updated**: 2025-10-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs

### Overview

This release contains the key components of the Neural Neighbourhood Selection
approach, as described in the paper. The provided code implements the main
components of the approach, covering data generation, training, and inference.
We also provide interfaces that are left to be implemented by the user so that
the code can be flexibly used in different contexts.

The following gives a brief overview of the contents; more detailed
documentation is available within each file:

*   __calibration.py__: Abstract timer for MIP solving.
*   __config_train.py__: Configuration file for training parameters.
*   __data_utils.py__: Utility functions for feature extraction.
*   __layer_norm.py__: Model layer normalisation and dropout utilities.
*   __light_gnn.py__: The GNN model used for training.
*   __local_branching_data_generation.py__: Library with functions required to
    generate imitation data.
*   __local_branching_expert.py__: Expert for Neural Large Neighbourhood Search
    based on local branching.
*   __mip_utils.py__: MIP utility functions.
*   __preprocessor.py__: Abstract APIs for MIP preprocessor.
*   __sampling.py__: Sampling strategies for Neural LNS.
*   __solution_data.py__: SolutionData classes used to log solution process.
*   __solvers.py__: Neural diving and neural neighbourhood selection
    implementations.
*   __solving_utils.py__: Common utilities for `solvers.py`.
*   __train.py__: Training script for neural neighbourhood selection model.
*   __data__: Directory with example tfrecord file to run training. The example dataset is derivative of open-sourced [NN Verification Dataset](https://github.com/deepmind/deepmind-research/tree/master/neural_mip_solving).

## Installation

To install the dependencies of this implementation, please run:

```
python3 -m venv /tmp/neural_lns_venv
source /tmp/neural_lns_venv/bin/activate
pip install -U pip
pip install -r requirements.txt
```


## Usage

1. Implement the interfaces provided in `calibration.py`, `preprocessor.py` and
   `solving_utils.py` for the timer, preprocessor / presolver and solver
   respectively.
2. Specify valid training and validation paths in `config_train.py` (i.e.
   <dataset_absolute_training_path> and <dataset_absolute_validation_path>).
3. Train the neural neighbourhood selection model using:

   ```
   cd <parent-directory-of-neural_lns>
   python3 -m neural_lns.train
   ```


## Citing this work

Paper: [Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs](https://arxiv.org/pdf/2107.10201.pdf)

If you use the code here please cite this paper:

    @article{sonnerat2021learning,
      title={Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs},
      author={Sonnerat, Nicolas and Wang, Pengming and Ktena, Ira and Bartunov, Sergey and Nair, Vinod},
      journal={arXiv preprint arXiv:2107.10201},
      year={2021}
    }

## Disclaimer

This is not an official Google product.