Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
This work addresses a specific theoretical bottleneck in graphical models for researchers in statistical inference, offering incremental improvements in convergence analysis.
The paper tackles the problem of analyzing convergence rates for smooth message passing algorithms in entropy-regularized MAP inference, providing a theoretical guarantee on the number of iterations needed to recover the true MAP solution under tight LP conditions.
Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sherali-Adams hierarchy are widely used to attempt to solve this problem, and smooth message passing algorithms have been proposed to solve regularized versions of these LPs with great success. This paper leverages recent work in entropy-regularized LPs to analyze convergence rates of a class of edge-based smooth message passing algorithms to $ε$-optimality in the relaxation. With an appropriately chosen regularization constant, we present a theoretical guarantee on the number of iterations sufficient to recover the true integral MAP solution when the LP is tight and the solution is unique.