Computer Science Department

CS 583: Approximation Algorithms

Chandra Chekuri

Spring 2016

Course Summary

Approximation algorithms for NP-hard problems are polynomial time heuristics that have guarantees on the quality of their solutions. Such algorithms are one robust way to cope with intractable problems that arise in many areas of Computer Science and beyond. In addition to being directly useful in applications, approximation algorithms allow us to explore the structure of NP-hard problems and distinguish between different levels of difficulty that these problems exhibit in theory and practice. A rich algorithmic theory has been developed in this area and deep connections to several areas in mathematics have been forged. The first third to half of the course will provide a broad introduction to results and techniques in this area with an emphasis on fundamental problems and widely applicable tools. The second half of the course will focus on more advanced and specialized topics.

Administrative Information

Lectures: Wed, Fri 11:00am-12.15pm in Siebel Center 1304.

Instructor:  Chandra Chekuri, 3228 Siebel Center, (chekuri at)

Teaching Assistant:  Vivek Madan, 3240 Siebel Center, (vmadan2 at)

Office Hours (Chandra): Tuesday, 1.30 - 2.30pm in 3228 Siebel

Office Hours (Vivek): Monday, 3 - 4pm in Siebel 3rd floor lounge

Grading Policy: There will be 6 homeworks, roughly once every two weeks. I expect all students to do the first 4. Students have the option of doing a course project in lieu of the last two homeworks (more information on topics forthcoming). Course projects could involve research on a specific problem or topic, a survey of several papers on a topic (summarized in a report and/or talk), or an application of approximation algorithms to some applied area of interest including experimental evaluation of specific algorithms.

Prerequisites: This is a graduate level class and a reasonable background in algorithms and discrete mathematics would be needed. Officially the prerequisite is CS 573 (now Theory II) or equivalent. Knowledge and exposure to probability and linear programming is necessary. The instructor will try to make the material accessible to non-theory students who might be interested in applications. Consult the instructor if you have questions.

Study material:

Tentative Topic List:


Note: The above list is tentative. Not all of the material can be covered in one semester.




Homework 0 (tex file) given on 01/20/2016, due in class on Friday 01/29/2016.

Homework 1 (tex file) given on 02/03/2016, due in class on Wednesday 02/17/2016.

Homework 2 (tex file) given on 02/17/2016, due in class on Wednesday 03/02/2016.

Homework 3 (tex file) given on 03/04/2016, due in class on Friday 03/18/2016.

Homework 4 (tex file) given on 03/28/2016, due in class on Friday 04/8/2016.

Homework 5 (tex file) given on 04/16/2016, due in class on Wednesday 04/27/2016.

Homework 6 (tex file) given on 04/27/2016, due on Monday 05/09/2016.


Warning: Notes may contain errors. Please bring those to the attention of the instructor.