Beyond the Worst-Case Analysis of Algorithms
Author(s): Tim Roughgarden
- Publisher: Cambridge University Press
- Publication Date: 14 Jan. 2021
- Language: English
- Print length: 704 pages
- ISBN-10: 1108494315
- ISBN-13: 9781108494311
Book Description
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Editorial Reviews
Review
‘Many important algorithmic problems are considered intractable according to the conventional worst-case metrics of computational complexity theory. This important book demonstrates that, for many such problems, it is possible to craft algorithms that perform well under plausible assumptions about the structure of the inputs that are likely to be presented. It may well mark a turning point in the field of algorithm design and analysis.’ Richard M. Karp, University of California at Berkeley
‘The worst-case analysis sets a criteria for perfect algorithmic performance. It has led and will continue to lead to the creation of breakthrough algorithms unthinkable by previous generations. But the success of worst-case analysis as the main theoretical computing framework has also placed provably-good algorithm design in a quandary, because nearly all practically significant problems have been shown to be intractable under such perfect criteria. Going beyond the worst-case analysis is a much-needed step for the theory of computing. This book – broad in scope and united by a common theme – represents diverse efforts in the field, and will elevate this fundamental subject for connecting computing theory with the rapid advances in Big Data and AI Solutions.’ Shanghua Teng, University of Southern California
‘The book is a must have for any aspiring algorithm researcher … Essential.’ D. Papamichail, Choice Magazine
Book Description
Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.
About the Author
Tim Roughgarden is a Professor of Computer Science at Columbia University. For his research, he has been awarded the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Kalai Prize in Computer Science and Game Theory, the Social Choice and Welfare Prize, the Mathematical Programming Society’s Tucker Prize, and the EATCS-SIGACT Gödel Prize. He was an invited speaker at the 2006 International Congress of Mathematicians, the Shapley Lecturer at the 2008 World Congress of the Game Theory Society, and a Guggenheim Fellow in 2017. His other books include Twenty Lectures on Algorithmic Game Theory (2016) and the Algorithms Illuminated book series (2017-2020).
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