logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) The Mutually Beneficial Relationship of Graphs and Matrices by Richard A. Brualdi ISBN 9780821853153, 0821853155

  • SKU: EBN-56777234
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

5.0

9 reviews
Instant download (eBook) The Mutually Beneficial Relationship of Graphs and Matrices after payment.
Authors:Richard A. Brualdi
Pages:110 pages.
Year:2011
Editon:New ed.
Publisher:American Mathematical Soc.
Language:english
File Size:2.87 MB
Format:pdf
ISBNS:9780821853153, 0821853155
Categories: Ebooks

Product desciption

(Ebook) The Mutually Beneficial Relationship of Graphs and Matrices by Richard A. Brualdi ISBN 9780821853153, 0821853155

Graphs and matrices enjoy a fascinating and mutually beneficial relationship. This interplay has benefited both graph theory and linear algebra. In one direction, knowledge about one of the graphs that can be associated with a matrix can be used to illuminate matrix properties and to get better information about the matrix. Examples include the use of digraphs to obtain strong results on diagonal dominance and eigenvalue inclusion regions and the use of the Rado-Hall theorem to deduce properties of special classes of matrices. Going the other way, linear algebraic properties of one of the matrices associated with a graph can be used to obtain useful combinatorial information about the graph. The adjacency matrix and the Laplacian matrix are two well-known matrices associated to a graph, and their eigenvalues encode important information about the graph. Another important linear algebraic invariant associated with a graph is the Colin de Verdiere number, which, for instance, characterizes certain topological properties of the graph. This book is not a comprehensive study of graphs and matrices. The particular content of the lectures was chosen for its accessibility, beauty, and current relevance, and for the possibility of enticing the audience to want to learn more.
*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products