Includes Index

1. Introduction
2. Parameterized algorithms
3. From adaptive analysis to instance optimality
4. Resource augmentation
5. Perturbation resilience
6. Approximation stability and proxy objectives
7. Sparse recovery
8. Distributional analysis
9. Introduction to semi-random models
10. Semi-random stochastic block models
11. Random-order models
12. Self-improving algorithms
13. Smoothed analysis of local search
14. Smoothed analysis of the simplex method
15. Smoothed analysis of Pareto curves in multiobjective optimization
16. Noise in classification
17. Robust high-dimensional statistics
18. Nearest-neighbor classification and search
19. Efficient tensor decomposition
20. Topic models and nonnegative matrix factorization
21. Why do local methods solve nonconvex problems?
22. Generalization in overparameterized models
23. Instance-optimal distribution testing and learning
24. Beyond competitive analysis
25. On the unreasonable effectiveness of satisfiability solvers
26. When simple hash functions suffice
27. Prior-independent auctions
28. Distribution-free models of social networks
29. Data-driven algorithm design
30. Algorithms with predictions

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.