This book is designed to be a textbook for graduate-level courses in approximation algorithms. After some experience teaching minicourses in the area in the mid-1990s, we sat down and wrote out an outline of the book. Then one of us (DPW), who was at the time an IBM Research Staff Member, taught several iterations of the course following the outline we had devised, in Columbia University's Department of Industrial Engineering and Operations Research in Spring 1998, in Cornell University's School of Operations Research and Industrial Engineering in Fall 1998, and at the Massachusetts Institute of Technology's Laboratory for Computer Science in Spring 2000. The lecture notes from these courses were made available, and we got enough positive feedback on them from students and from professors teaching such courses elsewhere that we felt we were on the right track. Since then, there have been many exciting developments in the area, and we have added many of them to the book; we taught additional iterations of the course at Cornell in Fall 2006 and Fall 2009 in order to field test some of the writing of the newer results.

The courses were developed for students who have already had a class, undergraduate or graduate, in algorithms, and who were comfortable with the idea of mathematical proofs about the correctness of algorithms. The book assumes this level of preparation. The book also assumes some basic knowledge of probability theory (for instance, how to compute the expected value of a discrete random variable).