The problem of inferring Markov random fields (MRFs) with a sparsity or robustness prior can be naturally modeled as a mixed-integer program. This motivates us to study a general class of convex submodular optimization problems with indicator variables, which we show to be polynomially solvable in this paper. The key insight is that, possibly after a suitable reformulation, indicator constraints preserve submodularity. Fast computations of the associated Lovasz extensions are also discussed under certain smoothness conditions, and can be implemented using only linear-algebraic operations in the case of quadratic objectives.
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View On polynomial time solvability of combinatorial Markov random fields