Locating Eigenvalues in Graphs / SpringerBriefs in Mathematics (PDF)
Algorithms and Applications
(Sprache: Englisch)
This book focuses on linear time eigenvalue location algorithms for graphs. This subject relates to spectral graph theory, a field that combines tools and concepts of linear algebra and combinatorics, with applications ranging from image processing and data...
sofort als Download lieferbar
Printausgabe Fr. 59.00
eBook (pdf)
Fr. 22.00
inkl. MwSt.
- Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Locating Eigenvalues in Graphs / SpringerBriefs in Mathematics (PDF)“
This book focuses on linear time eigenvalue location algorithms for graphs. This subject relates to spectral graph theory, a field that combines tools and concepts of linear algebra and combinatorics, with applications ranging from image processing and data analysis to molecular descriptors and random walks. It has attracted a lot of attention and has since emerged as an area on its own.
Studies in spectral graph theory seek to determine properties of a graph through matrices associated with it. It turns out that eigenvalues and eigenvectors have surprisingly many connections with the structure of a graph. This book approaches this subject under the perspective of eigenvalue location algorithms. These are algorithms that, given a symmetric graph matrix M and a real interval I, return the number of eigenvalues of M that lie in I. Since the algorithms described here are typically very fast, they allow one to quickly approximate the value of any eigenvalue, which is a basic step in most applications of spectral graph theory. Moreover, these algorithms are convenient theoretical tools for proving bounds on eigenvalues and their multiplicities, which was quite useful to solve longstanding open problems in the area. This book brings these algorithms together, revealing how similar they are in spirit, and presents some of their main applications.
This work can be of special interest to graduate students and researchers in spectral graph theory, and to any mathematician who wishes to know more about eigenvalues associated with graphs. It can also serve as a compact textbook for short courses on the topic.
Autoren-Porträt von Carlos Hoppen, David P. Jacobs, Vilmar Trevisan
Carlos Hoppen holds a PhD in Combinatorics and Optimization from the University of Waterloo, Canada. He is an Associate Professor of Mathematics at the Federal University of Rio Grande do Sul (UFRGS), Brazil. His research focuses on probabilistic and extremal combinatorics, and spectral graph theory.David P. Jacobs is a Professor Emeritus of Computer Science at Clemson University, USA. He has done research in various areas including graph algorithms and spectral graph theory. In 2006, he visited UFRGS as a Fulbright Scholar.
Vilmar Trevisan is a Professor of Mathematics at UFRGS. He earned a PhD in Mathematics at Kent State University, USA. His research focuses on combinatorics and spectral graph theory. He is currently a Visiting Professor at the Università degli Studi di Napoli, Italy.
Bibliographische Angaben
- Autoren: Carlos Hoppen , David P. Jacobs , Vilmar Trevisan
- 2022, 1st ed. 2022, 136 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031116984
- ISBN-13: 9783031116988
- Erscheinungsdatum: 21.09.2022
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Grösse: 2.84 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kommentar zu "Locating Eigenvalues in Graphs / SpringerBriefs in Mathematics"
0 Gebrauchte Artikel zu „Locating Eigenvalues in Graphs / SpringerBriefs in Mathematics“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Locating Eigenvalues in Graphs / SpringerBriefs in Mathematics".
Kommentar verfassen