Ben Adcock (25 results)

Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Condition: Like New. First Edition. A firm and square hardback with sharp corners and strong joints, just showing a few very minor rubs. Hence a non-text page has a small 'damaged' stamp. Despite such this book is actually in nearly new condition. Thus the contents are crisp, fresh and tight. Also, no pen-marks and not from a library so no such stamps or labels. Now offered for sale at a very sensible price.…

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: new.

Condition: New.

Paperback. Condition: New. 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 presents 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. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website (www.sparse-hd-book.com).This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques. …

Condition: As New. Unread book in perfect condition.

Condition: New. New. In shrink wrap. Looks like an interesting title! 0.22.

Paperback / Softback. Condition: Brand New. 292 pages. 10.08x7.01x0.94 inches. In Stock.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New. 2021. Paperback. . . . . .

Condition: Used. 1st edition NO-PA16APR2015-KAP.

Condition: Used.

Condition: New.

Hardcover. Condition: new. Hardcover. Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging including compressed sensing, wavelets and optimization in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches. This book provides a practical introduction to compressive imaging (with examples and code), an overview of core topics, and a comprehensive, rigorous treatment of the subject. It caters to graduate students, postdocs and faculty in mathematics, computer science, physics and engineering who want to learn about modern imaging techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: Used.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. 2021. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New.

Condition: New. In English.

Hardcover. Condition: Brand New. 300 pages. 9.50x6.75x1.25 inches. In Stock.

Paperback. Condition: New. 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 presents 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. It develops main concepts in a mathematically rigorous manner, with full proofs given wherever possible, and it contains many numerical examples, each accompanied by downloadable code. The authors provide an extensive bibliography of over 350 relevant references, with an additional annotated bibliography available on the book's companion website (www.sparse-hd-book.com).This text is aimed at graduate students, postdoctoral fellows, and researchers in mathematics, computer science, and engineering who are interested in high-dimensional polynomial approximation techniques. …

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a practical introduction to compressive imaging (with examples and code), an overview of core topics, and a comprehensive, rigorous treatment of the subject. It caters to graduate students, postdocs and faculty in mathematics, computer sc.…