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Teaching Assistant: Yuan Li
Office hours: Thursday 2:00-6:00pm
Contact: ylif@gmu.edu, Office 365 ST II
Syllabus: pdf
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"An algorithm is a procedure (a finite set of well-defined instructions) for accomplishing some task which,
given an initial state, will terminate in a defined end-state" - from wikipedia, the free encyclopedia
Course Scope.
In this course, a thorough examination of several well-known techniques
that are used for the design and analysis of algorithms will be covered.
Topics to be covered include theoretical measures of algorithm complexity,
sorting and selection algorithms, greedy algorithms, divide and conquer
techniques, dynamic programming, graph algorithms, search strategies,
and an introduction to the theory of NP-completeness. Additional topics
may be covered if time permits. Students are expected to have taken prior
undergraduate courses in data structures, as well as
calculus and discrete mathematics.
Prerequisites.
CS 310 and CS 330 Calculus (MATH 113, 114, 213) and MATH 125
Required Textbook.
Introduction to the Design and Analysis of Algorithms by
Anany Levitin, Addison Wesley; 2nd edition (2006)
Grading. [Top]
- Quizzes (about every 1-2 weeks) or CS culture assignments
(please use this form) 10%
- Assignments 30%
- Midterm Exam 25%
- Final Exam 35%
Final grade: A (top 5, 14%), B (6~23, 50%), C (24~33, 30%), D or F (35~36, 6%)
Important Dates.
[Top]
- Spring Break (March 11 ~ 18)
- Midterm Exam (March 22 - will cover Chapters 1-6)
- Final Exam (May 15 - will cover the entire book but mainly from Chapters 7-12)
Weekly Schedule, Readings, Assignments, Notes.
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| Date | Lecture Notes |
Scope | Assignments | Quiz |
Jan 23 | Lecture 01 pdf |
Chapter 1.1-1.2 | Read Chapter 1.3-1.4 |
Jan 25 | Lecture 02 pdf |
Chapter 2.1 |
Jan 30 | Lecture 03 pdf |
Chapter 2.2-2.3 | HW#01 pdf (due Feb 06) |
Feb 01 | non-recursive: pdf recursive: pdf |
Chapter 2.3-2.5 | Read Chapter 2.6-2.7 |
pdf |
Feb 06 | Lecture 06 pdf |
Chapter 3 | HW#02 pdf (due Feb 13
Feb 15) |
Feb 08 | Lecture 07 pdf |
Chapter 4.1-4.2 | | pdf |
Feb 13 | Lecture 07 pdf |
Chapter 4.3-4.4 |
Feb 15 | Lecture 08 pdf
class cancelled (weather) |
Chapter 4.5-4.6 |
HW#03 pdf (due Feb 22 Mar 01) |
Feb 20 | Lecture 08 pdf
|
Chapter 4.5-4.6 |
Feb 22 | Lecture 09 pdf |
Chapter 5.1-5.3 |
Feb 27 | Lecture 10 pdf |
Chapter 5.4-5.6 |
Mar 01 | Lecture 11 pdf |
Chapter 6.1-6.2 | HW#04
pdf (due Mar 08) |
pdf |
Mar 06 | Lecture 12 pdf |
Chapter 6.3-6.4 |
Mar 08 | Lecture 13 pdf |
Chapter 6.5-6.6 | HW#05
pdf (due Mar 20) |
Mar 20 | Midterm review pdf |
Mar 22 | Midterm | Chapters 1.1-6.6 |
Mar 27 | Lecture 14: space-time tradoffs pdf |
Chapter 7 |
Mar 29 | Lecture 15: dynamic programming pdf |
Chapter 8.1-8.2 | HW#06
pdf (due Apr 05) |
Apr 03 | Lecture 16: dynamic programming pdf |
Chapter 8.3-8.4 |
Apr 05 | Lecture 17: greedy algorithms pdf |
Chapter 9.1.-9.2 | HW#07
pdf (due Apr 12) |
pdf |
Apr 10 | Lecture 18: greedy algorithms pdf |
Chapter 9.3-9.4 |
Apr 12 | Lecture 19: Iterative methods pdf |
Chapter 10.1.-10.2 | HW#08
pdf (due Apr 19) | pdf |
Apr 17 | Lecture 20: Iterative methods pdf |
Chapter 10.3-10.4 |
Apr 19 | Lecture 21: Limitations pdf |
Chapter 11.1.-11.2 | HW#09
pdf (due Apr 26) | pdf |
Apr 24 | Lecture 22: Limitations pdf |
Chapter 11.3-11.4 |
Apr 26 | Lecture 23: Coping with limitations pdf |
Chapter 12.1.-12.2 | HW#10
pdf (optional due May 03) |
pdf |
May 01 | Lecture 24: Coping with limitations pdf |
Chapter 12.3-12.4 |
May 03 | Final review pdf |
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May 15 | Final (7:30am~10:15am) | Chapters 1.1-12.4 |
Click this button
 to subscribe the calendar.
This calendar will be updated frequently during the semester.
Useful Resources
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List of Topics.
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- Fundamentals of problem solving (problem types, data structures)
- Analysis of Algorithm Efficiency (asymptotic notation)
- Brute Force Techniques (sorting, search, traveling salesmen)
- Divide and Conquer (merge sort, quicksort, binary search)
- Decrease and Conquer (graph traversals, insertion sort)
- Transform and Conquer (heapsort, balanced search trees, problem reduction)
- Dynamic Programming
- Greedy Techniques (MWST, Dijkstra, Huffman trees)
- Limitations of Algorithm Power (decision trees, lower bounds, P, NP)
- Coping with Limitations
Policies.
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All required assignments must be completed by the stated due date and time.
There will be absolutely no extensions for the homework (not even in the case of emergency).
Your lowest homework grade will not be counted towards your final grade.
Please note that all coursework is to be done independently.
Plagiarizing the homework will be penalized by maximum
negative credit and cheating on the exam will earn you an F in the course.
See the GMU Honor Code System and Policies at
http://www.gmu.edu/catalog/acadpol.html and http://www.cs.gmu.edu/honor-code.html.
You are encouraged to discuss the material BEFORE you do the assignment.
As a part of the interaction you can
discuss a meaning of the question or possible ways of approaching the solution.
The homework should be written strictly
by yourself. In case your solution is based on the important idea of someone else
please acknowledge that in your solution, to avoid any accusations.
You will be allowed to have one page (letter size) of notes for the midterm and two pages (one sheet)
for the final. No copying of anything from the textbook or another person is allowed. You can
write some things verbatim. You can also write your notes on the computer and print them.
The notes sheet will be handed in with the exam.
The quiz will be a closed book exam - no notes will be allowed.
You can also have up to two opportunities of making up your missed/failed quizzes by turning in
two CS culture assignments. A CS culture assignment is a one-page written summary
of a talk
(please use this form to complete your assignment)
from a CS seminar (see http://cs.gmu.edu/events/) that you attend during the Spring'07 semester.
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