Course Scope: 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 and algorithms, as well as calculus and discrete mathematics. Programming skills are also a prerequisite.
Prerequisites:
CS 310 and CS 330 Calculus (MATH 113,
114, 213) and MATH 125 Familiarity with a high-level programming language
Schedule, Homeworks, Handouts
Required Textbook:
Cormen, Leiserson & Rivest, Introduction
to Algorithms, McGraw Hill, 1990
Recommended Textbook:
S. Dasgupta, C.H.Papadimitriou and U.V. Vazirani: Algorithms
Course Requirements:
There will be a midterm examination, several
practice homework assignments, one programming projects and a comprehensive
final examination. All required assignments must be completed by the stated
due date and time. Late coursework will not be accepted and make-up tests
will not be given for missed examinations. Please note that all coursework
is to be done independently- see the GMU Honor Code System and Policies
at http://www.gmu.edu/catalog/acadpol.html .
Grading:
Homeworks and Quizes 30%
Midterm 30%
Final 30%
Programming Project 10%
Tentative List of Topics:
Topic | Chapter(s) |
Growth of Functions | 2 |
Summations and Recurrences | 3,4 |
Counting and Probability | 6 |
Sorting and Order Statistics | 7 - 10 |
Red-Black Trees | 14 |
Dynamic Programming | 16 |
Greedy Algorithms | 17 |
Graph Algorithms | 23 - 26 |
NP-Completeness and Approximation Algorithms | 36 - 37 |
Please Note: You are expected to be familiar with the material in Chapters 1, 11 - 13, 19.