Spring 2017

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 (ywu@engr.uconn.edu), ITE 235. Office hour: Monday and Wednesday,

10:30-12 pm and 2:00 to 3:30 pm or by appointment.

TA: Chong Chu ( chong.chu@engr.uconn.edu)

Parker Timmerman (parker.timmerman@uconn.edu)

Prerequisites. CSE 2100 (Data structure and intro. to algorithms) and CSE 2500

(Introduction to discrete systems). Some topics need some basic knowledge of probabilities.

Moreover, you need to know how to program. We will have programming assignments in this class.

You can use any programming language.

Textbook: The required textbook is: Algorithm Design by Kleinberg and Tardos, 2006.

Occasionally I will also use some materials from a classic algorithm textbook:

Introduction to Algorithms, 3rd edition

by Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford Stein. MIT Press, 2009.

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.

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.

Algorithms for graphs: basic algorithms, minimum spanning tree and shortest path.

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,

string matching, approximation algorithm and randomized algorithms.

For more details on planned topics, please look at the planned schedule (from the course web page).

Homework. 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 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.

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, .., 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.

Note: you need to acknowledge any source of ideas other than the textbook. You must always

write up the solutions on your own.

Programming 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 up to three programming assignments. In these

assignments, 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 sheet.

Grading. Homework and programming assignments (25%), exam 1 (20%), exam 2 (20%), and final (35%).