Towards an empirical and theoretical evaluation of gradient based approaches for finding kernel minimum enclosing balls

In this paper we introduce a projected gradient descent algorithm to find kernel minimum enclosing balls and compare it to a gradient based Frank-Wolfe algorithm. We base our comparison on empirical as well as theoretical observations of the two methods by comparing different aspects of their behaviors that involve runtime, stability, abilities to find novel datapoints as well as convergence rates.