Empirical Study on Geodesically Convex Optimization Techniques for Applications in Machine Learning

Geodesically convex optimization has recently attracted attention from the machine learning community due to the realization that some important problems that appear to be non-convex at first glance, are geodesically convex, if we introduce a suitable differential structure and a metric. In this thesis, the student is expected to conduct an empirical study on the performance of geodesic convex optimization methods for applications in machine learning, compared to the state-of-the-art non-convex optimization techniques.