Stochastic Frank Wolfe library for TensorFlow and PyTorch

This repository contains the Stochastic Frank Wolfe (SFW) implementation in TensorFlow and Pytorch that was developed alongside the two following publications:

Deep Neural Network Training with Frank-Wolfe (arXiv:2010.07243)

Authors: Sebastian Pokutta, Christoph Spiegel, Max Zimmer

Colab Notebooks to reproduce the exact experiments of the paper:

• Colab Notebook for visualization of constraints (TensorFlow)
• Colab Notebook for sparseness during training (TensorFlow)
• Colab Notebook for comparing stochastic Frank–Wolfe methods (TensorFlow)
• Colab Notebook for large network training (PyTorch)

In case you find the paper or the implementation useful for your own research, please consider citing:

@article{pokutta2020deep,
  title={Deep neural network training with frank-wolfe},
  author={Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  journal={arXiv preprint arXiv:2010.07243},
  year={2020}
}

Projection-Free Adaptive Gradients for Large-Scale Optimization (arXiv:2009.14114)

Authors: Cyrille W. Combettes, Christoph Spiegel, Sebastian Pokutta

Colab Notebooks to reproduce the exact experiments of the paper:

• Colab Notebook for convex objectives (not using this repository)
• Colab Notebook for non-convex objectives (TensorFlow)

In case you find the paper or the implementation useful for your own research, please consider citing:

@article{combettes2020projection,
  title={Projection-free adaptive gradients for large-scale optimization},
  author={Combettes, Cyrille W and Spiegel, Christoph and Pokutta, Sebastian},
  journal={arXiv preprint arXiv:2009.14114},
  year={2020}
}