Adaptive Methods for Linear Programming Decoding
Information Theory
2007-07-13 v1 math.IT
Abstract
Detectability of failures of linear programming (LP) decoding and the potential for improvement by adding new constraints motivate the use of an adaptive approach in selecting the constraints for the underlying LP problem. In this paper, we make a first step in studying this method, and show that it can significantly reduce the complexity of the problem, which was originally exponential in the maximum check-node degree. We further show that adaptively adding new constraints, e.g. by combining parity checks, can provide large gains in the performance.
Cite
@article{arxiv.cs/0703123,
title = {Adaptive Methods for Linear Programming Decoding},
author = {Mohammad H. Taghavi and Paul H. Siegel},
journal= {arXiv preprint arXiv:cs/0703123},
year = {2007}
}
Comments
22 pages, 8 figures. Submitted to IEEE Transactions on Information Theory