A Guide to Experimental Algorithmics

This guidebook is for those who want to use computational experiments to support their work in algorithm design and analysis.

Catherine C. McGeoch (Author)

9781107001732, Cambridge University Press

Hardback, published 30 January 2012

272 pages, 78 b/w illus.
24.1 x 16 x 2.1 cm, 0.6 kg

'This book provides guidelines and suggestions for performing experimental algorithmic analysis. It contains many examples and includes links to a companion website with code for some specific experiments … The book is a good read with generally good examples, and is short enough to be easily digested.' Jeffrey Putnam, Computing Reviews

Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? To answer these questions the book draws on ideas from algorithm design and analysis, computer systems, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. The book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst.edu/alglab) contains downloadable files, programs and tools for use in experimental projects.

1. Introduction
2. A plan of attack
3. What to measure
4. Tuning algorithms, tuning code
5. The toolbox
6. Creating analysis-friendly data
7. Data analysis.

Subject Areas: Software Engineering [UMZ], Algorithms & data structures [UMB], Data analysis: general [GPH]