Noisy Information and Computational Complexity
Auteurs
Paramètres
- 324pages
- 12 heures de lecture
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This book deals with the computational complexity of mathematical problems for which available information is partial, noisy and priced. The author develops a general theory of computational complexity of continuous problems with noisy information and gives a number of applications; he considers deterministic as well as stochastic noise. He also presents optimal algorithms, optimal information, and complexity bounds in different worst case, average case, mixed worst-average, average-worst, and asymptotic. Particular topics the existence of optimal linear (affine) algorithms, optimality properties of smoothing spline, regularization and least squares algorithms (with the optimal choice of the smoothing and regularization parameters), adaption versus nonadaption, and relations between different settings. The book integrates the work of researchers over the past decade in such areas as computational complexity, approximation theory, and statistics, and includes many new results as well. The author supplies two hundred exercises to increase the reader's understanding of the subject.
Achat du livre
Noisy Information and Computational Complexity, Leszek Plaskota
- Langue
- Année de publication
- 1996
- Reliure
- (rigide),
- État du livre
- Abîmé
- Prix
- 42,99 €
Modes de paiement
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- Titre
- Noisy Information and Computational Complexity
- Langue
- Anglais
- Auteurs
- Leszek Plaskota
- Éditeur
- Cambridge University Press
- Publié
- 1996
- Format
- rigide
- Pages
- 324
- ISBN10
- 0521553687
- ISBN13
- 9780521553681
- Séries
- Catégories
- Description
- This book deals with the computational complexity of mathematical problems for which available information is partial, noisy and priced. The author develops a general theory of computational complexity of continuous problems with noisy information and gives a number of applications; he considers deterministic as well as stochastic noise. He also presents optimal algorithms, optimal information, and complexity bounds in different worst case, average case, mixed worst-average, average-worst, and asymptotic. Particular topics the existence of optimal linear (affine) algorithms, optimality properties of smoothing spline, regularization and least squares algorithms (with the optimal choice of the smoothing and regularization parameters), adaption versus nonadaption, and relations between different settings. The book integrates the work of researchers over the past decade in such areas as computational complexity, approximation theory, and statistics, and includes many new results as well. The author supplies two hundred exercises to increase the reader's understanding of the subject.