MaxSAT-Based Cutting Planes for Learning Graphical Models

  • Paul Saikko
  • Brandon Malone
  • Matti JärvisaloEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9075)


A way of implementing domain-specific cutting planes in branch-and-cut based Mixed-Integer Programming (MIP) solvers is through solving so-called 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.


Bayesian Network Cutting Plane Linear Programming Relaxation Markov Network Bayesian Network Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Helsinki Institute for Information Technology, Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

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