Upper bounds on the theta function of random graphs

TL;DR

This paper introduces a new class of polynomial-time computable graph parameters that provide tighter upper bounds on the Lovasz theta function for random graphs, supported by heuristic and experimental evidence.

Contribution

The paper proposes a novel class of graph parameters that serve as upper bounds on the theta function and offers heuristic and experimental insights into their expected values in random graphs.

Findings

Heuristic analysis suggests the theta function is below 1.55√n for G_{n,1/2}.

Experimental sampling supports the proposed bounds.

New parameters are computationally efficient and theoretically grounded as upper bounds.

Abstract

The theta function of Lovasz is a graph parameter that can be computed up to arbitrary precision in polynomial time. It plays a key role in algorithms that approximate graph parameters such as maximum independent set, maximum clique and chromatic number, or even compute them exactly in some models of random and semi-random graphs. For Erdos-Renyi random $G_{n,1/2}$ graphs, the expected value of the theta function is known to be at most $2\sqrt{n}$ and at least $\sqrt{n}$. These bounds have not been improved in over 40 years. In this work, we introduce a new class of polynomial time computable graph parameters, where every parameter in this class is an upper bound on the theta function. We also present heuristic arguments for determining the expected values of parameters from this class in random graphs. The values suggested by these heuristic arguments are in agreement with results that we obtain experimentally, by sampling graphs at random and computing the value of the respective parameter. Based on parameters from this new class, we feel safe in conjecturing that for $G_{n,1/2}$, the expected value of the theta function is below $1.55 \sqrt{n}$. Our paper falls short of rigorously proving such an upper bound, because our analysis makes use of unproven assumptions.