  This is a report for reproducibility challenge of NeurlIPS 2019 on the paper
Competitive Gradient Descent (Schafer et al., 2019). The paper introduces a
novel algorithm for the numerical computation of Nash equilibria of competitive
two-player games. It avoids oscillatory and divergent behaviours seen in
alternating gradient descent. The purpose of this report is to critically
examine the reproducibility of the work by (Schafer et al., 2019), within the
framework of the NeurIPS 2019 Reproducibility Challenge. The experiments
replicated in this report confirms the results of the original study. Moreover,
this project offers a Python (Pytorch based) implementation of the proposed CGD
algorithm which can be found at the following public git repository:
(https://github.com/GopiKishan14/Reproducibility_Challenge_NeurIPS_2019)
