Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization

This paper improves the scalability of MPE inference in a class of graphical models with piecewise-linear and pieceswise-quadratic dependencies and linear constraints over continuous domains and derive algorithms based on a consensus-optimization framework.