Learning a Neural Pareto Manifold Extractor with Constraints

A scalable Pareto manifold approximation scheme for high-dimensional models, using Fritz-John Conditions for Constrained Multi-Objective Optimization problems.

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Install

git clone https://github.com/smjtgupta/SUHNPF.git
cd SUHNPF
pip install -r requirements.txt

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Experiments

• `demo.ipynb' contains the working logic of the solver
• Folder 'examples' contains the cases and solver files

Requirements

This codebase requires is designed around Python 3.7 and Tensorflow 2.1 Note that Tensorflow might cause issues with symbolic gradient depending on version. Update accordingly.

Paper

[Arxiv Paper]

Citation

To cite this work, please follow the given format.

@inproceedings{gupta2022learning,
  title={Learning a Neural Pareto Manifold Extractor with Constraints},
  author={Gupta, Soumyajit and Singh, Gurpreet and Bollapragada, Raghu and Lease, Matthew},
  booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
  year={2022}
}