CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: 1/28/11
Scribe: Lewis Tseng (2009)
Notes edited by instructor in 2011.
We introduce the use linear programming (LP) in the design and analysis of approximation
algorithms.The to
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: January 26, 2011
Scribe: Md. Abul Hassan Samee (2009)
Notes edited by instructor in 2011.
1
Introduction
We discuss two closely related NP Optimization problems, namely Set Cove
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: January 21, 2011
Scribe: Sungjin Im (2009)
Notes edited by instructor in 2011.
In the previous lecture, we had a quick overview of several basic aspects of approximation
algorit
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: February 9, 2011
Scribe: Kyle Fox (2009)
In this lecture we explore the Knapsack problem. This problem provides a good basis for
learning some important procedures used for appr
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: February 11, 2011
Scribe: CC
In the previous lecture we discussed the Knapsack problem. In this lecture we discuss other
packing and independent set problems.
1
Maximum Independ
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: March 2, 2011
Scribe: CC
Local search is a powerful and widely used heuristic method (with various extensions). In this
lecture we introduce this technique in the context of app
CS 598CSC: Approximation Algorithms
Instructor: Alina Ene
1
Lecture date: February 23 and 25, 2011
Scribe: Alina Ene
Scheduling on Unrelated Parallel Machines
We have a set J of n jobs, and a set M of m machines. The processing time of job i is pij on mac
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: February 18, 2011
Scribe: CC
In the previous lecture we discussed packing problems of the form maxcfw_wx | Ax 1, x cfw_0, 1n
where A is a non-negative matrix. In this lecture w