CS 583 Spring 2015
Analysis of Algorithms I

Lecture time: Tuesday 7:20 pm - 10:00 pm
Location: Planetary Hall 206
Course webpage: http://www.cs.gmu.edu/~lifei/teaching/cs583spring15
Credit: 3 

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

Office hours: Tuesday 1:00pm – 3:00pm

Teaching assistant: Indranil Banerjee, Room 4456, Engineering Building, email: ibanerje@masonlive.gmu.edu

Office hoursMonday 2:00pm – 4:00pm


News:

-        April 21: Assignment 7’s due date is changed to April 28.

-        April 14: Assignment 7 is released. The due date is April 21.

-        March 31: Assignment 6 is released. The due date is April 14.

-        March 24: Assignment 5 is released. The due date is March 31.

-        February 24: Assignment 4 is released. The due date is March 3.

-        February 10: Assignment 3 is released. The due date is February 24.

-        January 27: Assignment 2 is released. The due date is February 10.

-        January 20: Assignment 1 is released. The due date is January 27.


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

Dates

Topics

Lecture notes

Scopes

Assignments

Note

1

January 20

Introduction

Introduction

Chapters 1, 2

Assignment 1: page 11, Exercise 1.1-4, page 22, Exercise 2.1-3, page 29, Exercise 2.2-3, page 41, Problem 2-3

2

January 27

Asymptotic notation

Appendix A

Chapter 3

Assignment 2: page 62, Problem 3-4(a)-(g), page 87, Exercises 4.3-2, 4.3-8, page 92, Exercise 4.4-2, page 109, Problem 4-5

Assignment 1 is due

Last day to add classes

 

Last day to drop classes

3

February 3

Divide and conquer

Divide and conquer

Chapters 4

 

4

February 10

 

Probabilistic analysis and randomized algorithms

Appendix C

Chapter 5

Assignment 3: page 122, Exercise 5.2-3, page 129, Exercise 5.3-5, page 143, Problem 5-2, page 1194, Exercises C.2-1, C.2-6, C.2-9, C.2-10, page 1200, Exercises C.3-1, C.3-2, C.3-3, page 1215, Problem C-1

Assignment 2 is due

(Class cancelled)

February 17

 

 

 

 

5

February 24

 

Quicksort

Non-comparison-based sorting

Chapters 6, 7, 8

Assignment 4: page 154, exercise 6.1-5, page 166, exercise 6.5-7, 6.5-9, page 178, exercise 7.2-4, page 184, exercise 7.4-1, page 185, Problem 7-3

Assignment 3 is due

6

March 3

 

Order statistics

Appendix B

Chapters 9, 10, 11

 

Assignment 4 is due

Spring break

March 10

 

 

 

 

7

Midterm exam

March 17

 

 

Chapters 12, 13, 14

 

 

8

March 24

 

Dynamic programming

Chapter 15

Assignment 5: page 396, exercise 15.4-6, page 410, Problem 15-10. One more problem.

 

9

March 31

 

Greedy algorithms

Interval scheduling (pages 8-14)

Chapters 16, 22

Assignment 6: page 427, exercise 16.2-3, page 428, exercise 16.2-5, page 447, Problem 16-2, page 623, Problem 22-4

Assignment 5 is due

10

April 7

 

 

 

11

April 14

 

Amortized analysis

Competitive analysis

Chapter 17

Assignment 7: page 476, Problem 17-5

Two additional problems

Assignment 6 is due

12

April 21

 

Shortest path I

Shortest path II

Shortest path III

Chapters 24, 25

 

Assignment 7 is due

13

April 28

 

Maximum flow

Demo

Chapter 26

 

Assignment 7 is due

14

May 5

 

Maximum flow

 

 

15

Final exam

May 12 (7:30pm – 10:15pm)

 

 

 

 

 

 

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)

Grading policy:

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.

No late assignments graded.

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.

Academic honesty:

The integrity of the University community is affected by the individual choices made by each of us. GMU has an Honor Code with clear guidelines regarding academic integrity. Three fundamental and rather simple principles to follow at all times are that: (1) all work submitted be your own; (2) when using the work or ideas of others, including fellow students, give full credit through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment, ask for clarification. No grade is important enough to justify academic misconduct. 

Plagiarism means using the exact words, opinions, or factual information from another person without giving the person credit. Writers give credit through accepted documentation styles, such as parenthetical citation, footnotes, or endnotes. Paraphrased material must also be cited, using MLA or APA format. A simple listing of books or articles is not sufficient. Plagiarism is the equivalent of intellectual robbery and cannot be tolerated in the academic setting. If you have any doubts about what constitutes plagiarism, please see me.

Disability statement:

If you have a learning or physical difference that may affect your academic work, you will need to furnish appropriate documentation to the Disability Resource Center. If you qualify for accommodation, the DRC staff will give you a form detailing appropriate accommodations for your instructor.

In addition to providing your professors with the appropriate form, please take the initiative to discuss accommodation with them at the beginning of the semester and as needed during the term. Because of the range of learning differences, faculty members need to learn from you the most effective ways to assist you. If you have contacted the Disability Resource Center and are waiting to hear from a counselor, please tell me.