@inproceedings{a9666a29efb847d48e540f2a51f7e137,
title = "An alternating direction method for dual MAP LP relaxation",
abstract = "Maximum a-posteriori (MAP) estimation is an important task in many applications of probabilistic graphical models. Although finding an exact solution is generally intractable, approximations based on linear programming (LP) relaxation often provide good approximate solutions. In this paper we present an algorithm for solving the LP relaxation optimization problem. In order to overcome the lack of strict convexity, we apply an augmented Lagrangian method to the dual LP. The algorithm, based on the alternating direction method of multipliers (ADMM), is guaranteed to converge to the global optimum of the LP relaxation objective. Our experimental results show that this algorithm is competitive with other state-of-the-art algorithms for approximate MAP estimation.",
keywords = "Approximate Inference, Augmented Lagrangian Methods, Graphical Models, LP Relaxation, Maximum a-posteriori",
author = "Ofer Meshi and Amir Globerson",
year = "2011",
doi = "10.1007/978-3-642-23783-6_30",
language = "אנגלית",
isbn = "9783642237829",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "470--483",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2011, Proceedings",
edition = "PART 2",
note = "null ; Conference date: 05-09-2011 Through 09-09-2011",
}