1. Lecture 7
CSE 331
Sep 13, 2011

2. Online Office Hour today @9:45pm

3. Access via the blog

4. GS algorithm: Firefly Edition
Mal
Wash
Simon
Inara
Zoe
Kaylee 1 2 3 4 5 6 1

5. GS algo outputs a stable matching
Last lecture, GS outputs a perfect matching S
Lemma 2: S has no instability

6. Two obervations Obs 1: Once m is engaged he keeps getting
engaged to “better” women
Obs 2: If w proposes m’ first and then to m
(or never proposes to m) then she
prefers m’ to m

7. Proof of Lemma 2 By contradiction
w’ last proposed to m’
Assume there is an instability (m,w’) m w
m prefers w’ to w
w prefers m to m’ m’ w’

8. Contradiction by Case Analysis
Depending on whether w’ had proposed to m or not
Case 1: w’ never proposed to m w’ m
By Obs 2
w’ prefers m’ to m
Assumed w’ prefers m to m’
Source: 4simpsons.wordpress.com

9. Case 2: w’ had proposed to m
Case 2.1: m had accepted w’ proposal
m is now engaged to w
4simpsons.wordpress.com
Thus, m prefers w to w’
By Obs 1
Case 2.1: m had rejected w’ proposal
m was engaged to w’’ (prefers w’’ to w’)
By Obs 1
m is finally engaged to w (prefers w to w’’)
By Obs 1
m prefers w to w’
4simpsons.wordpress.com

10. Overall structure of case analysis
Did w’ propose to m?
Did m accept w’ proposal?
4simpsons.wordpress.com
4simpsons.wordpress.com
4simpsons.wordpress.com

11. Questions?

12. Reminder: HW 1 due Friday
PLEASE SUBMIT EACH PROBLEM ON SEPARATE SHEETS

13. Extensions Fairness of the GS algorithm
Different executions of the GS algorithm

14. Main Steps in Algorithm Design
Problem Statement
Problem Definition n!
Algorithm
“Implementation”
Analysis
Correctness Analysis

15. Definition of Efficiency
An algorithm is efficient if, when implemented, it runs quickly on real instances
Implemented where?
Platform independent definition
What are real instances?
Worst-case Inputs
N = 2n2 for SMP
Efficient in terms of what?
Input size N

16. Definition-II n! How much better? By a factor of 2?
Analytically better than brute force
How much better? By a factor of 2?

17. At most c.Nd steps (c>0, d>0 absolute constants)
Definition-III
Should scale with input size
If N increases by a constant factor,
so should the measure
Polynomial running time
At most c.Nd steps (c>0, d>0 absolute constants)
Step: “primitive computational step”

18. More on polynomial time
Problem centric tractability
Can talk about problems that are not efficient!

19. Reading Assignments
Sections 1.2, 2.1, 2.2 and 2.4 in [KT]

20. Asymptotic Analysis
Travelling Salesman Problem (

21. Which one is better?

22. Now?

23. And now?

24. The actual run times n!
100n2
Asymptotic View n2

25. Asymptotic Notation ≤ is O with glasses ≥ is Ω with glasses