HW1 SOLUTION
1. Chapter 2
Problem 2: Suppose you have algorithms with the six running times listed
below.(Assume these are the exact number of operations performed as a function
of the input size n.) Suppose you have a computer that can perform 1010 opera
c 2005 Society for Industrial and Applied Mathematics
SIAM J. DISCRETE MATH.
Vol. 19, No. 1, pp. 122134
TIGHTER BOUNDS FOR GRAPH STEINER TREE
APPROXIMATION
GABRIEL ROBINS AND ALEXANDER ZELIKOVSKY
Abstract. The classical Steiner tree problem in weighted gr
CS 598CSC: Approximation Algorithms
Instructor: Chandra Chekuri
Lecture date: January 30, 2009
Scribe: Kyle Fox
In this lecture we explore the Knapsack problem. This problem provides a good basis for
learning some important procedures used for approximati
Comp 260: Advanced Algorithms
Tufts University, Spring 2009
Prof. Lenore Cowen
Scribe: Jordan Crouser
Lecture 4: The Knapsack Problem
1
The Knapsack Problem Dened
We are given a set
S = a1 , a2 , . . . , an1 , an a collection of objects
s1 , s2 , . . . ,
Massachusetts Institute of Technology
18.434: Seminar in Theoretical Computer Science
Lecturer: Adriana Lopez
March 7, 2006
Steiner Trees and Forests
1
Steiner Tree
Problem Given an undirected graph G
<V, E A, a cost function c
E
Q , and a
partition of V
Massachusetts Institute of Technology 18.434: Seminar in Theoretical Computer Science
Lecturer: Lele Yu February 16, 2006
Lecture notes on Shortest Superstring Problem
So far we have studied the set covering problem, but not looked at any real life applic
,5
CMPUT 675: Approximation Algorithms
Fall 2011
Lecture 4, 5 (Sep 20, Sep 22, 2011 ): Set Cover, LP Duality, 0-1 Kanpsack
Lecturer: Mohammad R. Salavatipour
Scribe: Amritpal Saini
This week we see two other algorithms for approximating set cover: one usi
15-451 S10: Quiz 2
Name:
Andrew id:
Closed book. One sheet of notes allowed. You should have four pages. You have 30 minutes
budget your time carefully.
When you are not sure what is meant by a question, just write down how you interpret it. If your
inte
Ecient approximation algorithms
for the Subset-Sums Equality problem
Cristina Bazgan
Miklos Santha
Universit Paris-Sud, LRI, bt 490
e
a
F91405 Orsay, France,
bazgan@lri.fr
CNRS, URA 410,
Universit Paris-Sud, LRI, bt 490,
e
a
F91405 Orsay, France,
santha@l