CSE 3500: Algorithms and Complexity
This course is for undergraduate students to learn basic concepts
and techniques in
algorithms and complexity.
Lectures. Monday and
Wednesday, 3:35-4:50 pm.
Instructor: Yufeng Wu
(email@example.com), ITE 235. Office hour: Monday and Wednesday,
10:00-12:00 am or by
CSE 2100 (Data structure and intro. to algorithms) and CSE 2500
(Introduction to discrete systems). Some topics need some basic knowledge
Moreover, you need to know how to program. We may have programming
assignments in this class.
You can use any programming language for these assignments.
The required textbook is: Algorithm Design by Kleinberg and
Several chapters are available online at the above web page.
Many lectures are based on this book, although we will sometimes
cover topics not in the book (where notes and handout will be
provided) and of course
we can not cover every chapter of the book.
Occasionally I will also use some
materials from a classic algorithm textbook:
to Algorithms, 3rd edition
by Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford
Stein. MIT Press, 2009.
Outline. 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.
Graph related topics.
2) Basic algorithm techniques. Divide and conquer. Dynamic programming. Greedy
Algorithms for graphs: basic algorithms, minimum spanning tree and
3) Advanced algorithms and complexity. NP-completeness. Algorithms for NP-complete problems.
If time permits, the following topics may be covered: algorithms
on numbers, network flows,
approximation algorithm and randomized algorithms.
For more details on planned topics, please look at the planned
schedule (from the course web page).
We will have homework every one to two weeks. Work
hard on them, even though
homework does not carry big weights on grading. This is the best
way to learn the materials
and prepare for exams. Much of what you learn from this course
comes from the homework.
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 difficulty is.
You are required to submit the homework electronically (in PDF
format). We appreciate greatly
if you typeset the homework. One good typesetting tool is Latex. Latex is a
bit difficult to pick up
but it is very powerful. Every computer scientist I know uses
Latex. You can get started on Latex from here.
Or you may type the English part and write by hand, very clearly,
any mathematical parts, and then scan it.
you need to acknowledge any source of ideas other than the
textbook. You must always
write up the solutions on your own.
How to write up an algorithm? A frequently asked
question is how to write up an algorithm for homework or exams.
Here is my suggestion. Do not write detailed code, and try to
limit the amount of pseudo-code you write.
The way I like to see a written algorithm is: (i) have a clear
structure (i.e. mark the main steps 1, 2, 3, ..,
but note I won't have too many steps: that can be very confusing
and ensure control flow such as loops as clearly stated), and (ii)
use concise English sentences
to explain what each step is supposed to do; you can use Math
symbols here but don't over-use them.
Check out the lecture notes to see how algorithms are usually
written. You should note that
it is almost always necessary to explain the following aspects:
(i) why you algorithm is correct?
(ii) how fast does your algorithm run (expressed in asymptotic
notations)? For example,
if you are asked to design e.g. a linear-time algorithm, you have
to justify why your algorithm
runs in linear time. Again, you should never give an algorithm
(e.g. for its correctness and running time).
assignment: while this course is mostly theoretical, I
have found that it can be useful to
put what is covered in class into action. I plan to assign one
programming assignment. In this
assignment, you will have chances to implement algorithms and also
evaluate their performance.
Exams. There are two in-class exams for this
course and a final exam. Exams will be closed book and notes
but you are allowed one (two for the final) 8 1/2 by 11 review
Homework and programming assignments (15%), exam 1 (20%), exam 2
(25%), and final (40%).
integrity: students in this class are expected to follow
the academic integrity code of UCONN.
For homework, you may discuss with other students about the
general ideas. But you must write up the
to do well in this class? Learning algorithm is not easy.
I have the following advice based on my
past experience in teaching this course. The most important thing to do for
this class is thinking.
Don't just copy down what I said in class. It is far more
important to think and follow the logic. This class
is about learning problem solving skills using algorithm. Getting
such skills is far more important than knowing
or memorizing a specific algorithm.