

Brief Course Description  
This course has twomain components: (a) linear programming and network flows, (c) NPcompleteness and approximation algorithms. We shall spend roughly one half of the semester on each topic. 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, network flows, and approximation techniques. In addition to that, students are expected to gain substantial discrete mathematics problem solving skills essential for computer engineers and scientists. The textbooks are meant mainly for references. We shall cover many topics not covered in the texts. Appropriate lecture notes shall be given. This course is highly mathematical in nature. One aim is for students to be able to formulate a practical problems mathematically, and find familiar techniques to solve them if possible. 

Class Syllabus


Prerequisites: A solid background on
basic algorithms. (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: Tuesdays &
Thursdays 08:00  09:20, Clemen 04..


Required Textbook 1: Vijay Vazirani, Approximation Algorithms, SpringerVerlag, 397 pages hardcover, ISBN: 3540653678, published 2001. 

Required Textbook 2: Vašek
Chvátal, Linear Programming, W. H. Freeman, 1983; Paperback,
1st ed., 478pp. ISBN: 0716715872 Publisher: W. H. Freeman Company, January
1983 

Recommended Reference books:

