CSE 431/531: Syllabus

CSE 431/531 Analysis of Algorithms

Syllabus

Professor-in-charge: Dr. Jinhui Xu

    Office: 315 Davis Hall
    Regular office hours: Fri. 11:00 - 11:50am
    Phone: (716) 645-4734
    E-mail:   jinhui@buffalo.edu

Teaching Assistants :

    Minghua Wang
      Office hours: Mon. Wed. 11:00 - 11:50am
      E-mail:   minghuaw@buffalo.edu

Lecture: MWF, 9:10-10:00pm, online
Course web site: http://www.cse.buffalo.edu/~jinhui/courses/cse531
Course forum: piazza

Required Texts:

Introduction to Algorithms (3e), Thomas H. Cormen, Charles E. Leiserson, Ronald Rivest, and Clifford Stein, MIT Press, 2001
Algorithms: Sequential & Parallel, Russ Miller and Lawrence Boxer, Prentice Hall PTR,1999

Recommended Texts:

Michael Goodrich and Roberto Tamassia, Algorithm Design: Foundations,Analysis, and Internet Examples, John Wiley & Sons, 2002
Network Flows, Theory, Algorithms, and Applications, R. K. Ahuja, T.L. Magnanti, and J. B. Orlin, Prentice Hall, 1993.

Prerequisites: CSE 250, Caculus II, a course that requires formal proofs.

Course Description:

     This course introduces basic elements of the design and analysis of computer algorithms. Topics include
      asymptotic notations and analysis, divide and conquer, greedy methods and
      dynamic programming, basic graph algorithms, NP-completeness, approximation algorithms, and
      network flows analysis. For each topic, beside in-depth coverage, one or more representative problems and
      their algorithms shall be discussed.

In addition to the design and analysis of algorithms, students are expected to gain substantial discrete
mathematics problem solving skills essential for computer engineers.

Main Objective:

Grasp the essential ideas of algorithm analysis and design

asymptotic notations analysis
typical algorithm design methodologies: divide-and-conquer, greedy algorithms, dynamic programming, network flow analysis
basic graph algorithms: BFS, DFS, MST, ...
the notions of NP-completeness and approximation algorithms

Gain substantial problem solving skills in designing algorithms

At the end of this course, each student should be able to:

Have a good overall picture of algorithm analysis and design techniques
Solve simple to moderately difficult algorithmic problems arising in applications
Understand the notions of NP-completeness and approximation algorithms
Be able to demonstrate the hardness of simple NP-complete problems

Class Participation 5% in total
Homeworks (6 problem sets) 25% in total
Midterm 20%
Final Exam 50%

Note: Each component will receive a numerical score. The course grade will be based on the weighted total of all components and the class curve.
Class participation includes attendance, participation in class discussions, and general interest towards the course material.

The exams will be closed-book, and closed-notes.

Academic Integrity Policy:

No collaboration, cheating, and plagiarism is allowed in homeworks, and exams. Those found violating academic integrity will get an immediate F in the course, and further actions, consistent with the Department's Academic Integrity Policy (page 35 of the graduate handbook) will be taken against them.