  We develop a Bregman proximal gradient method for structure learning on
linear structural causal models. While the problem is non-convex, has high
curvature and is in fact NP-hard, Bregman gradient methods allow us to
neutralize at least part of the impact of curvature by measuring smoothness
against a highly nonlinear kernel. This allows the method to make longer steps
and significantly improves convergence. Each iteration requires solving a
Bregman proximal step which is convex and efficiently solvable for our
particular choice of kernel. We test our method on various synthetic and real
data sets.
