This guidebook is written for anyone – student, researcher, or practitioner – who wants to carry out computational experiments on algorithms (and programs) that yield correct, general, informative, and useful results. (We take the wide view and use the term “algorithm” to mean “algorithm or program” from here on.)

Whether the goal is to predict algorithm performance or to build faster and better algorithms, the experiment-driven methodology outlined in these chapters provides insights into performance that cannot be obtained by purely abstract means or by simple runtime measurements. The past few decades have seen considerable developments in this approach to algorithm design and analysis, both in terms of number of participants and in methodological sophistication.

In this book I have tried to present a snapshot of the state-of-the-art in this field (which is known as experimental algorithmics and empirical algorithmics), at a level suitable for the newcomer to computational experiments. The book is aimed at a reader with some undergraduate computer science experience: you should know how to program, and ideally you have had at least one course in data structures and algorithm analysis. Otherwise, no previous experience is assumed regarding the other topics addressed here, which range widely from architectures and operating systems, to probability theory, to techniques of statistics and data analysis

A note to academics: The book takes a nuts-and-bolts approach that would be suitable as a main or supplementary text in a seminar-style course on advanced algorithms, experimental algorithmics, algorithm engineering, or experimental methods in computer science.