Computer Science & Engineering Department, SUNY at Buffalo
Brief Course Description

This course covers a variety of techniques for designing approximation algorithms. Three central themes are: (a) linear programming based techniques, (b) combinatorial methods, (c) randomized algorithms. Recent results on hardness of approximation, approximate counting, and semidefinite programming might also be touched upon from time to time. We shall spend roughly a third of the semester on each theme. We shall attempt to cover a broad range of commonly faced optimization problems, mostly on graphs, which can be naturally modelled and/or solved using linear programming, combinatorial, and randomization techniques. In addition to that, students are expected to gain substantial discrete mathematics problem solving skills essential for computer engineers and scientists.

The textbook is meant mostly to be a reference. We shall cover many topics not covered in the texts. Appropriate lecture notes shall be given.

This course is mathematical in nature. One aim is for students to be able to formulate practical problems mathematically, and find familiar techniques to solve them if possible.

Class Syllabus
Prerequisites: A solid background on basic algorithms and NP-completeness theory. (A formal course like CSE531 suffices.) Ability to read and quickly grasp new discrete mathematics concepts and results. Ability to do rigorous formal proofs.
Teaching staff and related info
  • Instructor
    • Dr. Hung Q. Ngo (
    • Office Hours: Tuesday & Thursday 10:30-11:30am in 238 Bell Hall. Generally, you could stop by any time if my door is opened. If the door is closed, then either I'm not in or I do not wish to be disturbed. However, please try your best to come during office hours. For questions that other students might potentially be interested in, please use the class news group at sunyab.cse.594, it is also more efficient that way. Your classmates can also answer your questions.

Place and Time: Mondays, Wednesdays, Fridays 9:00-9:50, Talbert 115.

Required Textbook: Vijay Vazirani, Approximation Algorithms, Springer-Verlag, 397 pages hardcover, ISBN: 3-540-65367-8, published 2001.


Recommended Reference books:
  • Vašek Chvátal, Linear Programming, W. H. Freeman, 1983; 478pp. ISBN: 0716715872, W. H. Freeman Company, January 1983.
  • Dorit Hochbaum (Editor), Approximation Algorithms for NP-Hard Problems, 624 pages ; Brooks/Cole Pub Co; ISBN: 0534949681; 1st edition (July 26, 1996)
  • Alexander Schrijver, Theory of Linear and Integer Programming, 484pp. ISBN: 0471982326, Wiley, John & Sons, Incorporated, June 1998.
  • Michael R. Garey and David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, 338pp. ISBN: 0716710455, W. H. Freeman Company, November 1990.
  • Rajeev Motwani and Prabhakar Raghavan, Randomized Algorithms, 492 pages, Cambridge University Press (August 25, 1995), ISBN: 0521474655

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