Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bregman Method for Structure Learning on Sparse Directed Acyclic
  Graphs   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.

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The categories of this paper are: eess
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol