Homework
Homework  Post Date  Due Date  Solution 
Homework 0  Example only; no need for submission  Solution 0 
Instructor
Rong
Ge
D226 LSRC
Email: rongge@cs.duke.edu
Office Hour (tentative): Tuesdays 4:30  5:30 PM, Fridays 3:00  4:00
PM at
LSRC D226
Teaching
Assistant
Haoming Li
Email: haoming.li@duke.edu
Shweta Patwa
Email: sjpatwa@cs.duke.edu
Chenwei Wu
Email: cwwu@cs.duke.edu
TA office hours
Sundays: 57PM in Bio Sci 130 with ChenweiRecitations: Check your individual recitation sessions.
Text Book:
Lecture notes will be uploaded on the course website after every lecture. In addition, the following books cover most of the syllabus:Prerequisites:
There are two hard prerequisites (equivalent courses are also acceptable):
Evaluation:
The grades will be determined by homework, two midterm exams and final exam. All exams are inclass closedbook exams.Homework:
There will be 9 homeworks. In normal cases
homeworks are due
one week after they are posted. The worst grade out of 9 homeworks
will be dropped.
Homework will be submitted through GradeScope. Homework should be typed
and submit as a PDF file. Latex is highly preferred.
Please make sure to read the guideline
on collaboration. Every incidence of cheating will be reported.
Questions about homework problems should be posted to Piazza
(instead of emailing the TAs or the instructor).
Synopsis:
Date  Contents  Notes  Homework  References 
1/10  Intro: Algorithms, Asymptotic Notations  Slides Board Scribe 

Basic Algorithm Design Techniques  
1/15  Divide and Conquer  Slides Board 
DPV
2, KT 5, CLRS 4 

1/17  Slides 
Homework 1  
1/22  Dynamic
Programming 
DPV
6, KT 6, CLRS: 15 

1/24  Homework 2  
1/29  
1/31  Greedy Algorithm  Homework 3  DPV 5, KT 4, CLRS: 16  
2/5  
2/7  
2/12  Review  
2/14  MidTerm
Exam 1 (in class) All previous materials. 

Graph Algorithms  
2/19  Graph representations and traversal  DPV 3, KT 3, CLRS 22  
2/21  Homework 4  
2/26  Shortest Path Algorithms 
DPV: 4.6, 6.6 KT: 6.8 CLRS: 24.1, 25 

2/28  Minimum Spanning Tree  Homework 5  DPV: 5 KT: 4 CLRS: 16, 23  
3/5  
3/7  Bipartite Graphs, Maximum Matching  Homework 6  DPV 7 KT: 7 CLRS: 26  
Fall Break  
Linear Programming  
3/19  Linear Programming, Relaxations  CLRS 29  
3/21  Duality  Homework 7  
3/26  Linear Programming Algorithms  
3/28  Review  
4/2  MidTerm
Exam 2 (in class) Graph algorithms and Linear Programming.  
Topics: Randomized Algorithms and Amortized Analysis  
4/4 
Basic Probabilities, Quicksort revisited, fast selection  DPV:
virtural chapger KT: 13 CLRS: 5, 11 

4/9  Hashing  
4/11  Amortized Analysis  Homework 8  KT 4.6 CLRS 17, 20  
Intractability  
4/16  P vs NP, reductions 
DPV:
8 KT: 8 CLRS: 34 

4/18 
More reductions 
Homework 9  
4/23  Even more reductions 

5/3  Final Exam 2:00  5:00 PM Everything covered in class except for bipartite matching and amortized analysis 