Mathematics for the Analysis of Algorithms - Paperback

by Daniel H. Greene (Author), Donald E. Knuth (Author)

This monograph collects some fundamental mathematical techniques that are required for the analysis of algorithms. It builds on the fundamentals of combinatorial analysis and complex variable theory to present many of the major paradigms used in the precise analysis of algorithms, emphasizing the more difficult notions. The authors cover recurrence relations, operator methods, and asymptotic analysis in a format that is concise enough for easy reference yet detailed enough for those with little background with the material. The book contains a wealth of highly original, instructive problems and solutions taken from actual examinations given at Stanford in various computer science courses.

Back Jacket

A quantitative study of the efficiency of computer methods requires an in-depth understanding of both mathematics and computer science. This monograph, derived from an advanced computer science course at Stanford University, builds on the fundamentals of combinatorial analysis and complex variable theory to present many of the major paradigms used in the precise analysis of algorithms, emphasizing the more difficult notions. The authors cover recurrence relations, operator methods, and asymptotic analysis in a format that is terse enough for easy reference yet detailed enough for those with little background. Approximately half the book is devoted to original problems and solutions from examinations given at Stanford.

"...a very valuable collection of mathematical techniques for the analysis of algorithms..." -- Mathematical Reviews

"The book covers the important mathematical tools used in computer science, especially in the exact analysis of algorithms. A wide range of topics are covered, from the binomial theorem to the saddle point method and Laplace's techniques for asymptotic analysis...The book is very well written. The style and the mathematical exposition make the book pleasant to read...It covers many of the major paradigms used in the analysis of algorithms in its one hundred plus pages." -- SIAM Review

"The book presents a welcome selection a