Resources

Text Book

• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms (fourth edition) The MIT Press, 2022. This will be referred to as CLRS4.

• The second edition errata or third can also be used. The third edition will be referred to as CLRS3.

Note that it is now possible to access library resources online without being on the University network or using a VPN. You will be prompted for you CAS login. You need to go through the library search system. For the fourth edition follow this CLRS4 and for the third edition follow this CLRS3.

Even when the book is available for free via the University library I recommend that you buy the book. It is one of the best textbooks on algorithms available.

At the moment most of the reading guides refer to the third edition, but I will update the reading guide for the fourth edition as I go along.

Reference Book

The following book, underlying most of the slides that have been used in previous years, can also be used but does not cover Section 4.3 and Chapters 21 and 32 of CLRS3:

• Jon Kleinberg and Éva Tardos. Algorithm Design Addison-Wesley, 2006.

Algorithms and Data Structures

Some useful books and links

• Algorithm Design: Foundations, Analysis, and Internet Examples, a textbook by Michael T. Goodrich and Roberto Tamassia, published by John Wiley & Sons in 2001

• Algorithms, a textbook by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani, published by McGraw-Hill in 2007

• VisuAlgo, visualising data structures and algorithms through animation, a project led by Steven Halim

• Data Structure Visualizations by David Galles

• Dictionary of Algorithms and Data Structures by Paul E. Black

• The Stony Brook Algorithm Repository by Steven Skiena, based on his textbook The Algorithm Design Manual, second edition, published by Springer-Verlag in 2008

Python

The learning of sufficient skills on the Python programming language is within your time budget for this course, but this should not take more than a couple hours. To the best of our knowledge, almost no student on this course has officially learned Python at UU, so nobody is at a disadvantage.

• Python programming language: official website; the skeleton codes for all the assignment problems are written in Python 3.

LaTeX and Demo Report

We highly recommend you learn or use LaTeX for typesetting your assignment reports in a professional way, but this is optional. The learning of LaTeX is outside your time budget for this course, but very well-invested as you will find out during the course or later. Here are some LaTeX resources:

• The demo report and LaTeX source, which also contain examples of all LaTeX commands you need for this course.

• LaTeX

• The (Not So) Short Introduction to LaTeX2e.

• The LaTeX wikibook a gentle but thorough introduction.

• Don’t know the LaTeX code for that mathematical symbol you need? Draw it by hand at Detexify, and the applet will find the code for you.

• clrscode3e package for typesetting algorithms as in CLRS3.

Extra Slides for Revision

When Pierre Flener taught the course he used slides by Kevin Wayne. It is difficult to understand the slides on your own, but they do contain a number of worked examples. Your primary tool for revision should be reading the relevant sections in the textbook.