EECS 336 Design and Analysis of Algorithms

Course news group

Time & Place

TTh 930-1050 TECH LG76


Prof. Hai Zhou
haizhou AT
L461 Tech Institute
Office Hours: Th 11am-1pm or by appointment


Mr. Debasish Das
dda902 AT
M314 Tech Institute
Office Hours: M 2-5 or by appointment


This course will cover the theoretical aspects of algorithm design and analysis. Topics include correctness proofs and performance analysis; number-theory algorithms (cryptography and hashing); divide-and-conquer; greedy algorithms; dynamic programming; linear programming and duality; graph algorithms; NP-completeness and reductions; and quantum algorithms. Besides a deep understanding of useful algorithms, a main goal of this course is to improve the problem solving abilities of the students. Therefore, if possible, we will study together how an efficient algorithm is designed.


EECS 310 and EECS 311.

Required Text

Reference Book


Grading: 40% Homework     30% Take-home exam     30% Final exam
Late policy: -40% per day


Homework 0: bio and background
Homework 1 (1/9 out, 1/16 due): algorithm correctness and analysis
Homework 2 (1/16 out, 1/23 due): arithmic and cryptography algorithms
Homework 3 (1/23 out, 1/30 due): divide-and-conquer and graphs
Homework 4 (1/30 out, 2/6 due): divide-and-conquer and path algorithms
Homework 5 (2/6 out, 2/13 due): greedy algorithms
Homework 6 (2/13 out, 2/20 due): greedy algorithms and dynamic programming
Take home exam (2/20 out, 2/27 due): pdf
Homework 7 (2/27 out, 3/6 due): max flow and linear programming
Homework 8 (3/6 out, 3/8 due): NP-completeness and approximation

Lecture schedule

Week 1: Algorithm design, correctness proofs, and performance analysis. READING: chapters 0,1

Week 2: Primality and cryptography. READING: chapter 1

Week 3: Divide-and-conquer algorithms. READING: chapter 2

Week 4: Graph algorithms. READING: chapters 3,4

Week 5: Greedy algorithms. READING: chapter 5

Week 6: Dynamic programming. READING: chapter 6

Week 7: Linear programming and duality. READING: chapter 7

Week 8: NP-completeness and reductions. READING: chapter 8

Week 9: Approximation and heuristic algorithms. READING: chapter 9

Week 10: Quantum algorithms. READING: chapter 10

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