Conic programming to understand sums of squares of eigenvalues of graphs

Fuente: arXiv
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Main Authors: Coutinho, Gabriel, Spier, Thomás Jung, Zhang, Shengtong
Format: Preprint
Published: 2024
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author Coutinho, Gabriel
Spier, Thomás Jung
Zhang, Shengtong
author_facet Coutinho, Gabriel
Spier, Thomás Jung
Zhang, Shengtong
contents In this paper we prove a conjecture by Wocjan, Elphick and Anekstein (2018) which upper bounds the sum of the squares of the positive (or negative) eigenvalues of the adjacency matrix of a graph by an expression that behaves monotonically in terms of the vector chromatic number. One of our lemmas is a strengthening of the Cauchy-Schwarz inequality for Hermitian matrices when one of the matrices is positive semidefinite. A related conjecture due to Bollobás and Nikiforov (2007) replaces the vector chromatic number by the clique number and sums over the first two eigenvalues only. We prove a version of this conjecture with weaker constants. An important consequence of our work is a proof that for any fixed $r$, computing a rank $r$ optimum solution to the vector chromatic number semidefinite programming is NP-hard. We also present a vertex weighted version of some of our results, and we show how it leads quite naturally to the known vertex-weighted version of the Motzkin-Straus quadratic optimization formulation for the clique number.
format Preprint
id arxiv_https___arxiv_org_abs_2411_08184
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Conic programming to understand sums of squares of eigenvalues of graphs
Coutinho, Gabriel
Spier, Thomás Jung
Zhang, Shengtong
Combinatorics
Optimization and Control
Spectral Theory
In this paper we prove a conjecture by Wocjan, Elphick and Anekstein (2018) which upper bounds the sum of the squares of the positive (or negative) eigenvalues of the adjacency matrix of a graph by an expression that behaves monotonically in terms of the vector chromatic number. One of our lemmas is a strengthening of the Cauchy-Schwarz inequality for Hermitian matrices when one of the matrices is positive semidefinite. A related conjecture due to Bollobás and Nikiforov (2007) replaces the vector chromatic number by the clique number and sums over the first two eigenvalues only. We prove a version of this conjecture with weaker constants. An important consequence of our work is a proof that for any fixed $r$, computing a rank $r$ optimum solution to the vector chromatic number semidefinite programming is NP-hard. We also present a vertex weighted version of some of our results, and we show how it leads quite naturally to the known vertex-weighted version of the Motzkin-Straus quadratic optimization formulation for the clique number.
title Conic programming to understand sums of squares of eigenvalues of graphs
topic Combinatorics
Optimization and Control
Spectral Theory
url https://arxiv.org/abs/2411.08184