@unpublished{2928,
  abstract     = {     This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems. },
  author       = {Kolmogorov, Vladimir and Schoenemann, Thomas},
  booktitle    = {arXiv},
  title        = {{Generalized sequential tree-reweighted message passing}},
  doi          = {10.48550/arXiv.1205.6352},
  year         = {2012},
}