Stochastic Hamiltonian Methods for Smooth Games

Code to reproduce the experiments reported in the paper:

@article{loizou2020stochastic}
  title={Stochastic Hamiltonian Methods for Smooth Games},
  author={Loizou, Nicolas and Berard, Hugo and Jolicoeur-Martineau, Alexia and Vincent, Pascal and Lacoste-Julien, Simon and Mitliagkas, Ioannis},
  journal={International Conference on Machine Learning},
  year={2020}
}

Requirements

We provide a file requirements.yml with the list of requirements. To create a new conda environement with the correct requirements, run: conda env create -f requirements.yml

Bilinear Experiment

To reproduce the results of the bilinear experiment run: python run_bilinear.py [OUTPUT_PATH]

Note:

• This will take around 4 hours to complete.
• You can get faster results by decreasing the number of iterations with the option --num-iter [50000]
• You can also decrease the number of seeds with the option: --num-seeds [5] (using only 1 seed will reduce the amount of time it takes to complete by 5)
• You can also choose evaluate the different methods separately with the option: -m [MODE] where [MODE] can be ("shgd-constant", "shgd-decreasing", "shgd-biased, "svrh", "svre")

Sufficiently-bilinear Experiment

To reproduce the results of the sufficiently-bilinear experiment run: python run_sufficiently_bilinear.py [OUTPUT_PATH]

Note:

• This will take around 1 day to complete.
• You can get faster results by decreasing the number of iterations with the option --num-iter [200000]
• You can also decrease the number of seeds with the option: --num-seeds [5] (using only 1 seed will reduce the amount of time it takes to complete by 5)
• You can also choose evaluate the different methods separately with the option: -m [MODE] where [MODE] can be ("shgd-constant", "shgd-decreasing", "shgd-biased, "svrh", "svrh-restart, "svre")

To replicate the GAN experiments

• Run the jupyter notebook notebok/GAN.ipynb (This can be done by uploading GAN.ipynb to google Colab or making a copy of the read-only version here: https://colab.research.google.com/drive/16zh-Ma-vayaoHP6NhuWr6uJjoabDMsVb?usp=sharing)