16 research outputs found
Spectral pseudorandomness and the road to improved clique number bounds for Paley graphs
We study subgraphs of Paley graphs of prime order induced on the sets of
vertices extending a given independent set of size to a larger independent
set. Using a sufficient condition proved in the author's recent companion work,
we show that a family of character sum estimates would imply that, as , the empirical spectral distributions of the adjacency matrices of any
sequence of such subgraphs have the same weak limit (after rescaling) as those
of subgraphs induced on a random set including each vertex independently with
probability , namely, a Kesten-McKay law with parameter . We prove
the necessary estimates for , obtaining in the process an alternate
proof of a character sum equidistribution result of Xi (2022), and provide
numerical evidence for this weak convergence for . We also conjecture
that the minimum eigenvalue of any such sequence converges (after rescaling) to
the left edge of the corresponding Kesten-McKay law, and provide numerical
evidence for this convergence. Finally, we show that, once , this
(conjectural) convergence of the minimum eigenvalue would imply bounds on the
clique number of the Paley graph improving on the current state of the art due
to Hanson and Petridis (2021), and that this convergence for all
would imply that the clique number is .Comment: 43 pages, 1 table, 6 figure
Asymptotic analysis of semidefinite bounds for polynomial optimization and independent sets in geometric hypergraphs
The goal of a mathematical optimization problem is to maximize an objective (or minimize a cost) under a given set of rules, called constraints. Optimization has many applications, both in other areas of mathematics and in the real world. Unfortunately, some of the most interesting problems are also very hard to solve numerically. To work around this issue, one often considers relaxations: approximations of the original problem that are much easier to solve. Naturally, it is then important to understand how (in)accurate these relaxations are. This thesis consists of three parts, each covering a different method that uses semidefinite programming to approximate hard optimization problems. In Part 1 and Part 2, we consider two hierarchies of relaxations for polynomial optimization problems based on sums of squares. We show improved guarantees on the quality of Lasserre's measure-based hierarchy in a wide variety of settings (Part 1). We establish error bounds for the moment-SOS hierarchy in certain fundamental special cases. These bounds are much stronger than the ones obtained from existing, general results (Part 2). In Part 3, we generalize the celebrated Lovász theta number to (geometric) hypergraphs. We apply our generalization to formulate relaxations for a type of independent set problem in the hypersphere. These relaxations allow us to improve some results in Euclidean Ramsey theory