| Titre : |
Beyond worst-case analysis of algorithms |
| Type de document : |
texte imprimé |
| Auteurs : |
TIM ROUGHGARDEN |
| Editeur : |
cambridge : Cambridge |
| Année de publication : |
2021 |
| Importance : |
686P. |
| Présentation : |
COUV.ILL |
| Format : |
24CM. |
| ISBN/ISSN/EAN : |
978-1-108-49431-1 |
| Langues : |
Anglais (eng) Langues originales : Anglais (eng) |
| Catégories : |
004 Informatique. Science et technologie de l'informatique.:004.4 Logiciel. Programme:004.42 Programmation. Programmes d'ordinateur:004.421 Algorithmes pour élaboration du programme
|
| Tags : |
Algorithmique analyse au pire des cas analyse alternative smoothed analysis analyse amortie analyse paramétrée stabilité des instances clustering optimisation linéaire apprentissage automatique réseaux neuronaux performance des algorithmes complexité modèles d’analyse Tim Roughgarden Cambridge University Press |
| Index. décimale : |
004 Informatique |
| Résumé : |
"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"-- "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"-- |
Beyond worst-case analysis of algorithms [texte imprimé] / TIM ROUGHGARDEN . - cambridge : Cambridge, 2021 . - 686P. : COUV.ILL ; 24CM. ISBN : 978-1-108-49431-1 Langues : Anglais ( eng) Langues originales : Anglais ( eng)
| Catégories : |
004 Informatique. Science et technologie de l'informatique.:004.4 Logiciel. Programme:004.42 Programmation. Programmes d'ordinateur:004.421 Algorithmes pour élaboration du programme
|
| Tags : |
Algorithmique analyse au pire des cas analyse alternative smoothed analysis analyse amortie analyse paramétrée stabilité des instances clustering optimisation linéaire apprentissage automatique réseaux neuronaux performance des algorithmes complexité modèles d’analyse Tim Roughgarden Cambridge University Press |
| Index. décimale : |
004 Informatique |
| Résumé : |
"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"-- "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"-- |
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