CS 583 Fall 2016
Analysis of Algorithms I

Lecture time: Tuesday 7:20 pm-10:00 pm
Location: Art and Design Building L008

Course webpage: http://www.cs.gmu.edu/~lifei/teaching/cs583fall16
Credit: 3

Instructor: Fei Li, Room 5326, Engineering Building, email: lifei@cs.gmu.edu

Office hours: Wednesday 5:30pm-7:30pm

Teaching assistant: TBD

Office hours: TBD

News:

Course overview:

In this course, a thorough examination of several well-known techniques that are used for the design and analysis of algorithms will be covered. Topics to be covered include theoretical measures of algorithm complexity, sorting and selection algorithms, greedy algorithms, divide and conquer techniques, dynamic programming, graph algorithms, search strategies, and an introduction to the theory of NP-completeness. Additional topics may be covered if time permits. Students are expected to have taken prior undergraduate courses in data structures, as well as calculus and discrete mathematics.

Prerequisites:

CS 310 and CS 330 Calculus (MATH 113, 114, 213) and MATH 125. Please contact with the instructor if you are not sure.

Textbook:

Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, 3rd Edition (2009)

Course materials:

 Lectures Topics Lecture notes Scopes Assignments Note 1 Introduction Chapters 1.1 2 Chapter 2.2 Chapter 3.1, 3.2 Assignment 1: Page 39, Exercise 2.3-3, Page 62, Problem 3-4(a)-(g) 3 Divide and conquer Chapters 4 Assignment 1 due 4 Probabilistic analysis and randomized algorithms Appendix C Chapter 5 5 Assignment 2: Page 117, Exercise 5.1-3, Page 122, Exercises 5.2-4, 5.2-5 6 Chapters 6, 7, 8 Assignment 2 due 7 Appendix B Chapters 9, 10, 11 8 (Midterm exam) 9 Chapter 15 Assignment 3: Page 370, Exercise 15.1-3; Page 397, Exercise 15.4-5, Page 405, Problem 15-2 10 Interval scheduling (pages 8-14) Chapter 16 Assignment 3 due 11 12 Chapter 17 13 Chapters 22-25 14 Chapter 26 15 Final exam

Topics:

In this course, we will consider the algorithm design and analysis techniques of various problems coming from the following areas:

Function growth: O, theta, omega notation (CLRS 3)

Recurrence relations (CLRS 4)

Probabilistic analysis; randomized algorithms (CLRS 5)

Amortized analysis (CLRS 17)

Dynamic programming (CLRS 15)

Greedy algorithms (CLRS 16.1-3)

Sorting: heapsort, quicksort, mergesort (CLRS 2, 6, 7)

Non-comparison-based (CLRS 8)

Selection/order statistics (CLRS 9)

Data structures balanced binary search trees (CLRS 12, 13)

Graph algorithms: BFS/DFS (CLRS 22)

Minimum spanning tree (CLRS 23)

Shortest paths (CLRS 24, 25)

Maximum flow (CLRS 26.1-3)

Time complexity, NP-Complete (CLRS 34)

Midterm exam (30%)

Final exam (40%)

Assignments and quizzes (30%)

[100; 95] : A+; (95; 90] : A; (90; 85] : A-; (85; 80] : B+; (80; 75] : B; (75; 70] : B-; (70; 65] : C+; (65; 60] : C; (60; 0] : F

No make-up exams for missed tests.

Policies:

Hand in hard copies of assignments in class. Please note that all coursework is to be done independently. Plagiarizing the homework will be penalized by maximum negative credit and cheating on the exam will earn you an F in the course. See the GMU Honor Code System and Policies at http://www.gmu.edu/catalog/acadpol.html and http://www.cs.gmu.edu/honor-code.html. You are encouraged to discuss the material BEFORE you do the assignment. As a part of the interaction you can discuss a meaning of the question or possible ways of approaching the solution. The homework should be written strictly by yourself. In case your solution is based on the important idea of someone else please acknowledge that in your solution, to avoid any accusations.