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We develop a Bregman proximal gradient method for structure learning onlinear structural causal models. While the problem is non-convex, has highcurvature and is in fact NP-hard, Bregman gradient methods allow us toneutralize at least part of the impact of curvature by measuring smoothnessagainst a highly nonlinear kernel. This allows the method to make longer stepsand significantly improves convergence. Each iteration requires solving aBregman proximal step which is convex and efficiently solvable for ourparticular choice of kernel. We test our method on various synthetic and realdata sets.

A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs