Updated to follow the recommendations put forth by the ACM/SIGCSE 2001 task force, Analysis of Algorithms, Second Edition 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.
Features & Benefits
- The material is presented with clear and complete chapters and a number of exercises for each section to encourage the use of an active and cooperative learning methodology.
- All algorithms are presented in pseudo-code that is understandable to anyone with knowledge of the concepts of conditional statements, loops, and recursion.
- The text supports student preparation and learning, each chapter includes the prerequisites needed, the goals or skills that students should have upon completion, and suggestions for studying the material.
A concise writing style that introduces the reader to the software design issues of space and time efficiency.
Follows the latest ACM-IEEE Curriculum recommendations.
The text was written for a one-semester analysis of algorithms course.
- Analysis of Algorithms
- Algorithms and Data Structures
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
Appendix A Pseudorandom Number Table
Appendix B Psuedorandom Number Generation
Appendix C Results of Chapter Study Suggestions
Appendix D References
Jeffrey McConnell-Canisius College
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.