Algorithm Analysis: Effective Learning Methods (Photocopy Edition)

Author: (USA) Jeffrey J. McConnell
Publisher:
Publish Date: 2003-03-11
Features: The main goal of this book is to enhance readers' awareness of the impact of algorithms on program efficiency and to cultivate the necessary skills for analyzing algorithms in programs. The material in each chapter is presented in a way that promotes effective and collaborative learning methods. Through comprehensive discussions and complete mathematical derivations, the book helps readers maximize their understanding of fundamental concepts. The book includes numerous programming projects designed to engage students. All algorithms in the book are given in pseudocode, making them easy to understand for readers with knowledge of conditional expressions, loops, and recursion. The book introduces software design problems that are both space-efficient and time-efficient in a concise writing style. It also provides comprehensive teaching materials, offering solutions and background knowledge on how to teach the use of effective and collaborative learning methods. Jeffrey J. McConnell is a full-time professor at Canisius College and has served as the department head of the computer science department since 1990. He is an advocate of effective and collaborative learning methods. Since 1993, he has applied this approach in his teaching practice and has achieved significant success. He has authored three books in this field, established seven laboratories, and has made multiple keynote speeches at teaching seminars, as well as created specialized information websites. He has also published 14 works in the field of computer graphics. This book can be used as a textbook for students of computer science and related disciplines to learn computer algorithms, or as a reference for relevant technical personnel.
Content:
1. Fundamentals of Algorithm Analysis
2. Search and Lookup Algorithms
3. Sorting Algorithms
4. Numerical Algorithms
5. Matching Algorithms
6. Graph Algorithms
7. Parallel Algorithms
8. Non-deterministic Algorithms
9. Related Algorithm Techniques
Appendix A: Random Number Table
Appendix B: Pseudorandom Number Generation Methods
Appendix C: Results of Prescribed Input for Learning Examples in Each Chapter
Appendix D: References for Each Chapter