Literature DB >> 32025074

Universal Completability, Least Eigenvalue Frameworks, and Vector Colorings.

Chris Godsil1, David E Roberson2, Brendan Rooney3, Robert Šámal4, Antonios Varvitsiotis5,6.   

Abstract

An embedding i ↦ p i ∈ R d of the vertices of a graph G is called universally completable if the following holds: For any other embedding i ↦ q i ∈ R k satisfying q i T q j = p i T p j for i = j and i adjacent to j, there exists an isometry mapping q i to p i for all i ∈ V ( G ) . The notion of universal completability was introduced recently due to its relevance to the positive semidefinite matrix completion problem. In this work we focus on graph embeddings constructed using the eigenvectors of the least eigenvalue of the adjacency matrix of G, which we call least eigenvalue frameworks. We identify two necessary and sufficient conditions for such frameworks to be universally completable. Our conditions also allow us to give algorithms for determining whether a least eigenvalue framework is universally completable. Furthermore, our computations for Cayley graphs on Z 2 n ( n ≤ 5 ) show that almost all of these graphs have universally completable least eigenvalue frameworks. In the second part of this work we study uniquely vector colorable (UVC) graphs, i.e., graphs for which the semidefinite program corresponding to the Lovász theta number (of the complementary graph) admits a unique optimal solution. We identify a sufficient condition for showing that a graph is UVC based on the universal completability of an associated framework. This allows us to prove that Kneser and q-Kneser graphs are UVC. Lastly, we show that least eigenvalue frameworks of 1-walk-regular graphs always provide optimal vector colorings and furthermore, we are able to characterize all optimal vector colorings of such graphs. In particular, we give a necessary and sufficient condition for a 1-walk-regular graph to be uniquely vector colorable.
© The Author(s) 2017.

Entities:  

Keywords:  Least eigenvalue; Lovász theta number; Positive semidefinite matrix completion; Semidefinite programming; Universal rigidity; Vector colorings

Year:  2017        PMID: 32025074      PMCID: PMC6979529          DOI: 10.1007/s00454-017-9899-2

Source DB:  PubMed          Journal:  Discrete Comput Geom        ISSN: 0179-5376            Impact factor:   0.969


  1 in total

1.  Phase transitions in semidefinite relaxations.

Authors:  Adel Javanmard; Andrea Montanari; Federico Ricci-Tersenghi
Journal:  Proc Natl Acad Sci U S A       Date:  2016-03-21       Impact factor: 11.205

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.