Constrained Linear Representability of Polymatroids and Algorithms for Computing Achievability Proofs in Network Coding
Abstract
The constrained linear representability problem (CLRP) for polymatroids determines whether there exists a polymatroid that is linear over a specified field while satisfying a collection of constraints on the rank function. Using a computer to test whether a certain rate vector is achievable with vector linear network codes for a multi-source network coding instance and whether there exists a multi-linear secret sharing scheme achieving a specified information ratio for a given secret sharing instance are shown to be special cases of CLRP. Methods for solving CLRP built from group theoretic techniques for combinatorial generation are developed and described. These techniques form the core of an information theoretic achievability prover, an implementation accompanies the article, and several computational experiments with interesting instances of network coding and secret sharing demonstrating the utility of the method are provided.
Cite
@article{arxiv.1605.04598,
title = {Constrained Linear Representability of Polymatroids and Algorithms for Computing Achievability Proofs in Network Coding},
author = {Jayant Apte and John MacLaren Walsh},
journal= {arXiv preprint arXiv:1605.04598},
year = {2017}
}
Comments
submitted to IEEE Transactions on Information Theory, (this version: corrected figure 9)