Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale

ArXi:2605.05568v1 Announce Type: cross Despite the growing availability of large datasets, causal structure learning remains computationally prohibitive at scale. We revisit sparsest-permutation learning for linear structural equation models and show that exact Cholesky factorization is unnecessary for structure recovery. This observation motivates a -level relaxation that searches for sparse triangular factors over a precision- screening graph.