| Description |
xvii, 686 pages : illustrations ; 26 cm |
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text txt rdacontent |
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unmediated n rdamedia |
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volume nc rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Introduction / Tim Roughgarden -- Refinements of Worst-Case Analysis. Parameterized algorithms / Fedor Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi -- From adaptive analysis to instance optimality / Jérémy Barbay -- Resource augmentation / Tim Roughgarden -- Deterministic Models of Data. Perturbation resilience /Konstantin Makarychev and Yury Makarychev -- Approximation stability and proxy objectives / Avrim Blum -- Sparse recovery / Eric Price -- Semi-Random Models. Distributional analysis / Tim Roughgarden -- Introduction to semi-random models / Uriel Feige -- Semi-random stochastic block models / Ankur Moitra -- Random-order models / Anupam Gupta and Sahil Singla -- Self-improving algorithms / C. Seshadhri -- Smoothed Analysis. Smoothed analysis of local search / Bodo Manthey -- Smoothed analysis of the simplex method / Daniel Dadush and Sophie Huiberts -- Smoothed analysis of Pareto curves in multiobjective optimization / Heiko Röglin -- Applications in Machine Learning and Statistics. Noise in classification / Maria-Florina Balcan and Nika Haghtalab -- Robust high-dimensional statistics / Ilias Diakonikolas and Daniel Kane -- Nearest-neighbor classification and search / Sanjoy Dasgupta and Samory Kpotufe -- Efficient tensor decomposition Aravindan Vijayaraghavan -- Topic models and nonnegative matrix factorization / Rong Ge and Ankur Moitra -- Why do local methods solve nonconvex problems? / Tengyu Ma -- Generalization in overparameterized models / Moritz Hardt -- Instance-optimal distribution testing and learning / Gregory Valiant and Paul Valiant -- Further Applications. Beyond competitive analysis / Anna R. Karlin and Elias Koutsoupias -- On the unreasonable effectiveness of SAT solvers / Vijay Ganesh and Moshe Vardi -- When simple hash functions suffice / Kai-Min Chung, Michael Mitzenmacher and Salil Vadhan -- Prior-independent auctions / Inbal Talgam-Cohen -- Distribution-free models of social networks / Tim Roughgarden and C. Seshadhri -- Data-driven algorithm design / Maria-Florina Balcan -- Algorithms with predictions / Michael Mitzenmacher and Sergei Vassilvitskii. |
| Summary |
"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"--Provided by publisher. |
| Subject |
Computer algorithms.
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Computer programming.
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Algorithms.
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Algorithms. (OCoLC)fst00805020
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Computer algorithms. (OCoLC)fst00872010
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Computer programming. (OCoLC)fst00872390
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| Added Author |
Roughgarden, Tim, editor.
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| ISBN |
9781108494311 hardcover |
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1108494315 hardcover |
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9781108637435 electronic publication |
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