Beyond the Worst-Case Analysis of Algorithms -

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Résumé :
Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.

Sommaire:
Forward; Preface; 1. Introduction Tim Roughgarden; Part I. Refinements of Worst-Case Analysis: 2. Parameterized algorithms Fedor Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi; 3. From adaptive analysis to instance optimality J?r?my Barbay; 4. Resource augmentation Tim Roughgarden; Part II. Deterministic Models of Data: 5. Perturbation resilience Konstantin Makarychev and Yury Makarychev; 6. Approximation stability and proxy objectives Avrim Blum; 7. Sparse recovery Eric Price; Part III. Semi-Random Models: 8. Distributional analysis Tim Roughgarden; 9. Introduction to semi-random models Uriel Feige; 10. Semi-random stochastic block models Ankur Moitra; 11. Random-order models Anupam Gupta and Sahil Singla; 12. Self-improving algorithms C. Seshadhri; Part IV. Smoothed Analysis: 13. Smoothed analysis of local search Bodo Manthey; 14. Smoothed analysis of the simplex method Daniel Dadush and Sophie Huiberts; 15. Smoothed analysis of Pareto curves in multiobjective optimization Heiko R?glin; Part V. Applications in Machine Learning and Statistics: 16. Noise in classification Maria-Florina Balcan and Nika Haghtalab; 17. Robust high-dimensional statistics Ilias Diakonikolas and Daniel Kane; 18. Nearest-neighbor classification and search Sanjoy Dasgupta and Samory Kpotufe; 19. Efficient tensor decomposition Aravindan Vijayaraghavan; 20. Topic models and nonnegative matrix factorization Rong Ge and Ankur Moitra; 21. Why do local methods solve nonconvex problems? Tengyu Ma; 22. Generalization in overparameterized models Moritz Hardt; 23. Instance-optimal distribution testing and learning Gregory Valiant and Paul Valiant; Part VI. Further Applications: 24. Beyond competitive analysis Anna R. Karlin and Elias Koutsoupias; 25. On the unreasonable effectiveness of satisfiability solvers Vijay Ganesh and Moshe Vardi; 26. When simple hash functions suffice Kai-Min Chung, Michael Mitzenmacher and Salil Vadhan; 27. Prior-independent auctions Inbal Talgam-Cohen; 28. Distribution-free models of social networks Tim Roughgarden and C. Seshadhri; 29. Data-driven algorithm design Maria-Florina Balcan; 30. Algorithms with predictions Michael Mitzenmacher and Sergei Vassilvitskii.