scholarly journals Spectral symmetry in conference matrices

Author(s):  
Willem H. Haemers ◽  
Leila Parsaei Majd

AbstractA conference matrix of order n is an $$n\times n$$ n × n matrix C with diagonal entries 0 and off-diagonal entries $$\pm 1$$ ± 1 satisfying $$CC^\top =(n-1)I$$ C C ⊤ = ( n - 1 ) I . If C is symmetric, then C has a symmetric spectrum $$\Sigma $$ Σ (that is, $$\Sigma =-\Sigma $$ Σ = - Σ ) and eigenvalues $$\pm \sqrt{n-1}$$ ± n - 1 . We show that many principal submatrices of C also have symmetric spectrum, which leads to examples of Seidel matrices of graphs (or, equivalently, adjacency matrices of complete signed graphs) with a symmetric spectrum. In addition, we show that some Seidel matrices with symmetric spectrum can be characterized by this construction.

Filomat ◽  
2017 ◽  
Vol 31 (20) ◽  
pp. 6393-6400 ◽  
Author(s):  
E. Ghasemian ◽  
G.H. Fath-Tabar

Let G? be a signed graph with the underlying graph G and with sign function ? : E(G) ? {?}. In this paper, we characterize the signed graphs with two distinct eigenvalues whose underlying graphs are triangle-free. Also, we classify all 3-regular and 4-regular signed graphs whose underlying graphs are triangle-free and give their adjacency matrices as well.


2018 ◽  
Vol 9 (10) ◽  
pp. 1473-1476
Author(s):  
K. V. Madhusudhan
Keyword(s):  

2017 ◽  
Vol 340 (6) ◽  
pp. 1271-1286 ◽  
Author(s):  
Beifang Chen ◽  
Jue Wang ◽  
Thomas Zaslavsky
Keyword(s):  

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
Sho Kubota ◽  
Etsuo Segawa ◽  
Tetsuji Taniguchi

Author(s):  
Meng-Yue Cao ◽  
Jack H. Koolen ◽  
Akihiro Munemasa ◽  
Kiyoto Yoshino
Keyword(s):  

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1522
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang

The power method is commonly applied to compute the Perron vector of large adjacency matrices. Blondel et al. [SIAM Rev. 46, 2004] investigated its performance when the adjacency matrix has multiple eigenvalues of the same magnitude. It is well known that the Lanczos method typically requires fewer iterations than the power method to determine eigenvectors with the desired accuracy. However, the Lanczos method demands more computer storage, which may make it impractical to apply to very large problems. The present paper adapts the analysis by Blondel et al. to the Lanczos and restarted Lanczos methods. The restarted methods are found to yield fast convergence and to require less computer storage than the Lanczos method. Computed examples illustrate the theory presented. Applications of the Arnoldi method are also discussed.


2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Peikang Zhang ◽  
Baofeng Wu ◽  
Changxiang He
Keyword(s):  

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