Overview
A problem-based approach to learning Combinatorics
Very computational in its orientation, with an added emphasis on Proof technique
Written from the perspective of the student, with an abundance of exercises at the end of each section
All computer simulations prepared in open-source SAGE software
Includes supplementary material: sn.pub/extras
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Table of contents (11 chapters)
Keywords
About this book
Reviews
“The book is an excellent introduction to combinatorics. … The author uses a clear language and often provides an easy intuitive access to abstract subjects. The presentation is well motivated, the explanations are transparent and illustrated by carefully selected examples. Each section ends with a list of well formulated exercises which make the book ideally suited for self-instruction.” (Astrid Reifegerste, zbMATH 1328.05001, 2016)
“This book by Beeler … is an excellent introductory text on combinatorics. The author gives the right balance of theory, computation, and applications, and he presents introductory-level topics, such as the multiplication principle, binomial theorem, and distribution problems in a clear manner. … Summing Up: Highly recommended. Upper-division undergraduates through researchers and faculty.” (S. L. Sullivan, Choice, Vol. 53 (1), September, 2015)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: How to Count
Book Subtitle: An Introduction to Combinatorics and Its Applications
Authors: Robert A. Beeler
DOI: https://doi.org/10.1007/978-3-319-13844-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-13843-5Published: 27 March 2015
Softcover ISBN: 978-3-319-35508-5Published: 29 October 2016
eBook ISBN: 978-3-319-13844-2Published: 14 March 2015
Edition Number: 1
Number of Pages: XV, 361
Number of Illustrations: 59 b/w illustrations, 2 illustrations in colour
Topics: Combinatorics, Probability Theory and Stochastic Processes