CS 583 Fall 2013
Analysis of Algorithms I

Lecture time: Wednesday 7:20 pm - 10:00 pm
Robinson Hall B203
Course webpage: http://www.cs.gmu.edu/~lifei/teaching/cs583-fall13
Credit: 3 

Instructor: Fei Li, Room 5326, Engineering Building, email: mailto:lifei@cs.gmu.edu
Office hours: Wednesday 5:00pm 7:00pm


Teaching assistant: Yue Ning, Engineering Building, email: mailto:yning@gmu.edu

Office hours: Fridays 1:00pm 3:00pm


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.


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


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

Course materials:





Lecture notes





August 28



Appendix A

Chapter 10 (read it by yourself)


September 4

Divide and Conquer


September 11

Probabilistic Analysis



September 18





September 25



October 2





Midterm Exam

October 9


October 16

Dynamic Programming


October 23

Dynamic Programming




October 30

Greedy Algorithms


November 6

Amortized Analysis


November 13

Graph Traversals

Minimum Spanning Tree


November 20

Shortest Path

Thanksgiving recess

November 27


December 4

Maximum Flow



Final exam

December 11

7:30pm 10:15pm


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)

Course outcomes:

       An understanding of classical problems in Computer Science

       An understanding of classical algorithm design and analysis strategies

       An ability to analyze the computability of a problem

       Be able to design and analyze new algorithms to solve a computational problem

       An ability to reason algorithmically

Tentative grading:

       Midterm Exam (30%)

       Final Exam (40%)

       Assignments (30%)

       No make-up exams for missed tests.

       No late assignments graded.


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.