Beyond the Worst-Case Analysis of Algorithms

by Tim Roughgarden (Editor), Daniel A Spielman (Foreword)

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.

Genres: Computer Science, Algorithms

704 pages, Hardcover

Published February 25, 2021

Reviews

[Anthony O'Connor · Rating 5 out of 5]

Excellent and informative

A collection of review papers on alternatives to Worst-Case analysis. Fairly advanced so not a casual read. But worth the effort if you're interested in the subject.
The most interesting paper is on the unexpectedly good performance of SAT Solvers as n increases. Worst case performance is exponential but average case performance is much better. Why exactly? What's going on? -- Since SAT is NP-complete the implications of this are huge.