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Sparse polynomial approximation of high-dimensional functions / Ben Adcock, Simone Brugiapaglia, Clayton G. Webster

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Computational science & engineering ; 25Publisher: Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics, [2022]Description: 1 Online-Ressource (xviii, 292 pages)ISBN:
  • 9781611976885
Subject(s): Additional physical formats: 9781611976878 | Erscheint auch als: Sparse polynomial approximation of high-dimensional functions. Druck-Ausgabe Philadelphia : Society for Industrial and Applied Mathematics, 2022. xvii, 292 SeitenMSC: MSC: 65-01 | 65Dxx | 41AxxRVK: RVK: SK 280DOI: DOI: 10.1137/1.9781611976885Online resources: Summary: Over seventy years ago, Richard Bellman coined the term "the curse of dimensionality" to describe phenomena and computational challenges that arise in high dimensions. These challenges, in tandem with the ubiquity of high-dimensional functions in real-world applications, have led to a lengthy, focused research effort on high-dimensional approximation--that is, the development of methods for approximating functions of many variables accurately and efficiently from data. This book provides an in-depth treatment of one of the latest installments in this long and ongoing story: sparse polynomial approximation methods. These methods have emerged as useful tools for various high-dimensional approximation tasks arising in a range of applications in computational science and engineering. It begins with a comprehensive overview of best s-term polynomial approximation theory for holomorphic, high-dimensional functions, as well as a detailed survey of applications to parametric differential equations. It then describes methods for computing sparse polynomial approximations, focusing on least squares and compressed sensing techniques. Sparse Polynomial Approximation of High-Dimensional Functions is the first comprehensive and unified treatment of polynomial approximation techniques that can mitigate the curse of dimensionality in high-dimensional approximation, including least squares and compressed sensing; develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible; contains many numerical examples, each accompanied by downloadable code; and includes an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website ( PPN: 1805566652Package identifier: Produktsigel: ZDB-72-SIA
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