  We study the implicit bias of generic optimization methods, such as mirror
descent, natural gradient descent, and steepest descent with respect to
different potentials and norms, when optimizing underdetermined linear
regression or separable linear classification problems. We explore the question
of whether the specific global minimum (among the many possible global minima)
reached by an algorithm can be characterized in terms of the potential or norm
of the optimization geometry, and independently of hyperparameter choices such
as step-size and momentum.
