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We consider the maximum-a-posteriori inference problem in discrete graphicalmodels and study solvers based on the dual block-coordinate ascent rule. We mapall existing solvers in a single framework, allowing for a better understandingof their design principles. We theoretically show that some block-optimizingupdates are sub-optimal and how to strictly improve them. On a wide range ofproblem instances of varying graph connectivity, we study the performance ofexisting solvers as well as new variants that can be obtained within theframework. As a result of this exploration we build a new state-of-the artsolver, performing uniformly better on the whole range of test instances.

Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization