@inproceedings{SaikkoMJ:CPAIOR2015,
author = {Paul Saikko and Brandon Malone and Matti J\"arvisalo},
title = {{MaxSAT}-Based Cutting Planes for Learning Graphical Models},
booktitle = {Proceedings of the 12th International Conference on 
             Integration of Artificial Intelligence and 
             Operations Research Techniques in 
             Constraint Programming (CPAIOR 2015)},
editor = {Laurent Michel},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {9075},
pages = {345--354},
year = {2015},
}

Abstract:

A way of implementing domain-specific cutting planes in 
branch-and-cut based Mixed-Integer Programming (MIP) solvers is 
through solving so-cal led sub-IPs, solutions of which 
correspond to the actual cuts. We consider the suitability of 
using Maximum satisfiability solvers instead of MIP for solving 
sub-IPs. As a case study, we focus on the problem of learning 
optimal graphical models, namely, Bayesian and chordal Markov 
network structures.