scholarly journals Generalized Index Coding Problem and Discrete Polymatroids

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 646
Author(s):  
Anoop Thomas ◽  
Balaji Sundar Rajan

The connections between index coding and matroid theory have been well studied in the recent past. Index coding solutions were first connected to multi linear representation of matroids. For vector linear index codes, discrete polymatroids, which can be viewed as a generalization of the matroids, were used. The index coding problem has been generalized recently to accommodate receivers that demand functions of messages and possess functions of messages. In this work we explore the connections between generalized index coding and discrete polymatroids. The conditions that need to be satisfied by a representable discrete polymatroid for a generalized index coding problem to have a vector linear solution is established. From a discrete polymatroid, an index coding problem with coded side information is constructed and it is shown that if the index coding problem has a certain optimal length solution then the discrete polymatroid is representable. If the generalized index coding problem is constructed from a matroid, it is shown that the index coding problem has a binary scalar linear solution of optimal length if and only if the matroid is binary representable.

2013 ◽  
Vol 23 (2) ◽  
pp. 223-247 ◽  
Author(s):  
EDEN CHLAMTÁČ ◽  
ISHAY HAVIV

In theindex codingproblem, introduced by Birk and Kol (INFOCOM, 1998), the goal is to broadcast ann-bit word tonreceivers (one bit per receiver), where the receivers haveside informationrepresented by a graphG. The objective is to minimize the length of a codeword sent to all receivers which allows each receiver to learn its bit. Forlinearindex coding, the minimum possible length is known to be equal to a graph parameter calledminrank(Bar-Yossef, Birk, Jayram and Kol,IEEE Trans. Inform. Theory, 2011).We show a polynomial-time algorithm that, given ann-vertex graphGwith minrankk, finds a linear index code forGof lengthÕ(nf(k)), wheref(k) depends only onk. For example, fork= 3 we obtainf(3) ≈ 0.2574. Our algorithm employs a semidefinite program (SDP) introduced by Karger, Motwani and Sudan for graph colouring (J. Assoc. Comput. Mach., 1998) and its refined analysis due to Arora, Chlamtac and Charikar (STOC, 2006). Since the SDP we use is not a relaxation of the minimization problem we consider, a crucial component of our analysis is anupper boundon the objective value of the SDP in terms of the minrank.At the heart of our analysis lies a combinatorial result which may be of independent interest. Namely, we show an exact expression for the maximum possible value of the Lovász ϑ-function of a graph with minrankk. This yields a tight gap between two classical upper bounds on the Shannon capacity of a graph.


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