

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 NPcompleteness 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  


Place and Time: Mondays, Wednesdays,
Fridays 9:009:50, Talbert 115.


Required Textbook: Vijay Vazirani, Approximation Algorithms, SpringerVerlag, 397 pages hardcover, ISBN: 3540653678, published 2001.


Recommended Reference books:

