Algorithms and Complexity
This course is for undergraduate students to learn basic concepts and
algorithms and complexity.
Lectures. Tuesday and Thursday,
Wu (email@example.com), ITE 235. Office hour: Tuesday and
Prerequisites. CSE 2100 (Data
structure and intro. to algorithms) and CSE 2500
discrete systems). Some
topics need some basic
knowledge of probabilities.
The required textbook is: Introduction to Algorithms (3nd edition) by
T. Cormen, C. Leiserson,
R. Rivest, and C. Stein, 2009. Most of the lectures are based on this
book, although we will sometimes
cover some topics not in the book (where notes and handout will be
provided) and of course
we can not cover every chapter of the book.
This course is
lecture-based. The planned
subjects are the following (subject to change).
1) Fundamental concepts of algorithms. Asymptotic order of growth.
Basic running time analysis.
Divide and conquer. Basic probabilistic analysis. Different kinds of
2) Basic algorithm techniques. Dynamic programming. Greedy algorithms.
Algorithms for graphs:
basic algorithms, minimum spanning tree and shortest path.
3) Advanced algorithms and complexity.
Introduction to approximation
algorithms. Algorithms on strings.
We will have homework every one to two weeks. Work hard on them, although
homeworks do not carry big weights on grading. This is the best way to
prepare for exams.
Much of what you learn from this course comes from the homeworks. Your
solutions should be
concise, correct and legible. Some of the
problems may be challenging, depending on your
background. If you can not solve a problem, briefly explain where the
Note: you need to acknowledge
any source of ideas other than the textbook. You must always
solutions on your own.
There are two midterm exams for this course and a final exam. Exams
will be closed book.
We will have additional review sessions to help students prepare for
the exams. Be there if you can.
Homeworks (25%), mid-term 1 (20%), mid-term 2 (25%) and