1. 演算法 ( 課號 : 50339000 ) 內容 : 本課程是電機工程學系計算機類課程之必選課，對象以對利 用計算機來解決問題有興趣的同學為主。本課程主要是教授 基本的演算法分析與設計技巧，並整理及比較目前最重要之 演算法。 Theme: What is the best algorithm for a given problem Three things you will learn: 1.Design a good algorithm 2.Analyze (and verify) it 3.Lower bounds: know when to stop.

2. Grading 1. 作業 (10%) –3-4 homework 2. 程式 (15%) –1 or 2 program assignments 3. 期中考 (35%) 4. 期末考 (40%)

3. Course Information Instructor: 顏嗣鈞 –E-mail: yen@cc.ee.ntu.edu.tw –Office: Rm. 540, E.E. Building II –Phone: 2363 5251 ext. 540 –http://www.ee.ntu.edu.tw/~yen –Office Hours: by appointment Teaching Assistant: to be announced later Web site: http://www.ee.ntu.edu.tw/~yen/courses/algorithm01.html

4. Text Book T. Cormen, C. L eiserson, and R. R ivest, Introduction to Algorithms, MIT Press and McGraw- Hill Book Company, 1990. We will refer to this book as “CLR”

5. Reference Books G. Brassard and P. Bratley, Fundamentals of Algorithmics, Prentice-Hall, 1996.

6. Reference Books G. Rawlins, Compare to What? An Introduction to the Analysis of Algorithms, Computer Science Press, 1992.

7. Reference Books D. E. Knuth, The Art of Computer Programming, Vol. 1 and 3, Third Edition, Addison Wesley, 1997.

8. Reference Books D. Harel, The Science of Computing, Addison Wesley, 1987.

9. Reference Books Robert Sedgewick, Algorithms (in C, C++, Pascal), Second Edition, Addison Wesley, 1992

10. Reference Books U. Manber, Introduction to Algorithms, Addison Wesley, 1989.

11. Reference Books J. Nievergelt, Algorithms and Data Structures (With Applications to Graphics and Geometry), Prentice-Hall, 1993.

12. Reference Books B. Moret and H. Shapiro, Algorithms from P to NP, Vol. 1, The Benjamin/Cummings Publishing Company, Inc., 1991.

13. Topics Introduction to algorithm design and analysis. Techniques for algorithm analysis –Useful formulas. –Recurrence relations.

14. Topics Techniques for algorithm design –Divide and conquer, partitioning. –Dynamic programming. –Greedy algorithms. –Backtracking. –Tree based algorithms. –Recursion. –Approximations. –Problem Transformation, reduction. –Integer programming. –Probabilistic techniques.

15. Topics Sorting and searching algorithms. Geometric algorithms Graph algorithms. Pattern matching algorithms Cryptographic algorithms. Network flow algorithms. Intractable problems. –Introduction to computational complexity. –Reductions –NP completeness.

16. Topics If time permits, the following topics will be covered: Introduction to program verification. Introduction to approximation algorithms. Introduction to probabilistic algorithms. Introduction to parallel algorithms. Introduction to distributed algorithms.