Analysis of Algorithms / Edition 2

by Jeffrey McConnell

Overview

Updated to follow the recommendations put forth by the ACM/SIGCSE 2001 task force, Analysis of Algorithms raises awareness of the effects that algorithms have on the efficiency of a program and develops the necessary skills to analyze general algorithms used in programs. The text presents the material with the expectation that it can be used with active and cooperative learning methodology, based on the premise that students learn more effectively and retain more information longer when they are active participants in the learning process. To accomplish this, the chapters are clear and complete to encourage students to prepare by reading before class, and the text is filled with exciting examples and exercises that look at the efficiency of various algorithms to solve a problem. The author is well known for workshops that he presents on the active learning model. He has written an instructor's manual that helps instructors understand how to present the material in an active way.

Product Details

ISBN-13:	9780763707828
Publisher:	Jones & Bartlett Learning
Publication date:	11/16/2007
Edition description:	2E
Pages:	451
Product dimensions:	7.40(w) x 9.10(h) x 1.20(d)

About the Author

Jeffrey McConnell, Canisius College Jeffrey J. McConnell has a Ph.D. in Computer Science from Worcester Polytechnic Institute (1988), a M.S. in Computer Science from SUNY at Buffalo (1986), and a B.A. in Mathematics from Canisius College. Jeffrey J. McConnell is a full Professor at Canisius College where he has been a member of the faculty since 1983. He has served as department chair there since 1990. In 1993, he received the I. Joan Lorch Women's Studies Award for his contributions to women at Canisius College, and received a Robert H. Goddard Fellowship from Worcester Polytechnic Institute (1987-88). Dr. McConnell is a proponent of active and cooperative learning. He has incorporated this into his teaching pedagogy at a high level since 1993. He has four publications on active learning and has given 15 workshops on the topic. He was the featured speaker at George Washington University's 1997 Fall Teaching Colloquium and was the luncheon speaker for Rochester Institute of Technology's Insight 98 conference. He has created a web site with active and cooperative learning information. He also has 14 publications in the area of Computer Graphics.

Table of Contents

Chapter Preface
Chapter 1 Analysis Basics
Chapter 2 Recursive Algorithms
Chapter 3 Searching and Selection Algorithms
Chapter 4 Sorting Algorithms
Chapter 5 Numeric Algorithms
Chapter 6 Formal Language Algorithms
Chapter 7 Matching Algorithms
Chapter 8 Graph Algorithms
Chapter 9 Parallel Algorithms
Chapter 10 Limits of Computation
Chapter 11 Other Algorithmic Techniques
Chapter Appendix A Pseudorandom Number Table
Chapter Appendix B Psuedorandom Number Generation
Chapter Appendix C Results of Chapter Study Suggestions
Chapter Appendix D References
Chapter Index

Editorial Reviews

McConnell (Canisius College) developed this course to make students aware of the impact of algorithms on a program's efficiency and instruct them in the skills to analyze algorithms used in programs. Students learn the basics of analysis; searching, selecting, and sorting algorithms; and the elements of numeric, matching, graph, parallel, nondeterministic, greedy approximation, and probabilistic algorithms. Study suggestions and programming exercises are provided for students to apply what they learn. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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