Linear Index Coding via Semidefinite Programming

TL;DR

This paper presents a polynomial-time algorithm for linear index coding that leverages semidefinite programming and provides bounds related to graph minrank, improving understanding of graph capacity and coding efficiency.

Contribution

It introduces a novel polynomial-time algorithm for linear index coding based on SDP, with bounds tied to graph minrank, and offers a new combinatorial insight into Lovasz theta-function.

Findings

Algorithm finds linear index codes of length O(n^{f(k)}) for graphs with minrank k

Provides bounds on the Lovasz theta-function for graphs with given minrank

Establishes a tight gap between classical bounds on Shannon capacity

Abstract

In the index coding problem, introduced by Birk and Kol (INFOCOM, 1998), the goal is to broadcast an n bit word to n receivers (one bit per receiver), where the receivers have side information represented by a graph G. The objective is to minimize the length of a codeword sent to all receivers which allows each receiver to learn its bit. For linear index coding, the minimum possible length is known to be equal to a graph parameter called minrank (Bar-Yossef et al., FOCS, 2006). We show a polynomial time algorithm that, given an n vertex graph G with minrank k, finds a linear index code for G of length $\widetilde{O}(n^{f(k)})$, where f(k) depends only on k. For example, for k=3 we obtain f(3) ~ 0.2574. Our algorithm employs a semidefinite program (SDP) introduced by Karger, Motwani and Sudan (J. ACM, 1998) for graph coloring 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 an upper bound on 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 Lovasz theta-function of a graph with minrank k. This yields a tight gap between two classical upper bounds on the Shannon capacity of a graph.