LPQP for MAP: Putting LP Solvers to Better Use

06/18/2012 ∙ by Patrick Pletscher, et al. ∙ 0

MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence between the LP pairwise auxiliary variables, and QP equivalent terms given by the product of the unaries. We develop two efficient algorithms based on variants of this relaxation. The algorithms minimize the non-convex objective using belief propagation and dual decomposition as building blocks. Experiments on synthetic and real-world data show that the solutions returned by our algorithms substantially improve over the LP relaxation.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

Code Repositories

lpqp

Combined LP and QP relaxation for MAP inference


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.