Convergent tree-reweighted message passing for energy minimization

  • Vladimir Kolmogorov

MSR-TR-2004-90 |

Artificial Intelligence and Statistics

Tree-reweighted max-product message passing (TRW) is an algorithm for energy minimization introduced recently by Wainwright et al. [7]. It shares some similarities with Pearl’s loopy belief propagation. TRW was inspired by a problem of maximizing a lower bound on the energy. However, the algorithm is not guaranteed to increase this bound – it may actually go down. In addition, TRW does not always converge. We develop a modification of this algorithm which we call sequential tree-reweighted message passing. Its main property is that the bound is guaranteed not to decrease. We also give a weak tree agreement condition which characterizes local maxima of the bound with respect to TRW algorithms. We prove that our algorithm has a limit point that achieves weak tree agreement. Experimental results demonstrate that on certain synthetic and real problems our algorithm outperforms both the ordinary belief propagation and tree-reweighted algorithm [7].