Solving combinatorial optimization problems using relaxed linear programming: a high performance computing perspective

Authors:
Chen Jin;Qiang Fu;Huahua Wang;Ankit Agrawal;William Hendrix;Wei-keng Liao;Md. Mostofa Ali Patwary;Arindam Banerjee;Alok Choudhary
Affiliations:
Northwestern University;University of Minnesota, Twin Cities;University of Minnesota, Twin Cities;Northwestern University;Northwestern University;Northwestern University;Northwestern University;University of Minnesota, Twin Cities;Northwestern University
Venue:
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Year:
2013

Citing 13
Cited 0

Randomized rounding: a technique for provably good algorithms and algorithmic proofs

Combinatorica - Theory of Computing
Parallel Optimization: Theory, Algorithms and Applications

Parallel Optimization: Theory, Algorithms and Applications
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem

SIAM Journal on Discrete Mathematics
Convergent Tree-Reweighted Message Passing for Energy Minimization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models, Exponential Families, and Variational Inference

Foundations and Trends® in Machine Learning
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes

The Journal of Machine Learning Research
Pregel: a system for large-scale graph processing

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
An alternating direction method for dual MAP LP relaxation

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Emerging topic detection using dictionary learning

Proceedings of the 20th ACM international conference on Information and knowledge management
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing

SIAM Journal on Scientific Computing
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Foundations and Trends® in Machine Learning
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several important combinatorial optimization problems can be formulated as maximum a posteriori (MAP) inference in discrete graphical models. We adopt the recently proposed parallel MAP inference algorithm Bethe-ADMM and implement it using message passing interface (MPI) to fully utilize the computing power provided by the modern supercomputers with thousands of cores. The empirical results show that our parallel implementation scales almost linearly even with thousands of cores.