Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization

This repo containts the code to reproduce the experiments of our paper: Kiran Koshy Thekumparampil, Niao He, and Sewoong Oh. Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization, AISTATS 2022.

Please refer the paper for more details.

System prerequisites

• python 3.6.13
• numpy 1.19.4
• scipy 1.5.3
• matplotlib 3.3.1
• pandas 1.1.4
• oct2py 5.2.0
• sklearn 0.23.1
• jupyter

Reproducing the experimental results

Python script for reproducing the experiments for solving

1. Synthetic Qudratic problems (Figs. 1(a) and 1(b)) is present in lifted_primal_dual_quadratic_probs.ipynb

2. Reinforcement Learning Policy Evaluation problems (Figs. 1(c) and 1(d)) is present in lifted_primal_dual_RL_policy_eval_probs.ipynb

Please run the cells in the jupyter notebooks serially from top to bottom.

Acknowledgement

We use the same copy of policy trace as used in [Du+17] to construct the MSPBE minimization problem. We obtained the data through private communication and it is stored in the folder mountaincar_data

[Du+17] Simon S. Du et al. "Stochastic variance reduction methods for policy evaluation." International Conference on Machine Learning. PMLR, 2017.