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Verlag: Society for Industrial and Applied Mathematics,U.S., US, 2023
ISBN 10: 1611977398 ISBN 13: 9781611977394
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Designed for a proof-based course on linear algebra, this rigorous and concise textbook intentionally introduces vector spaces, inner products, and vector and matrix norms before Gaussian elimination and eigenvalues so students can quickly discover the singular value decomposition (SVD)-arguably the most enlightening and useful of all matrix factorizations. Gaussian elimination is then introduced after the SVD and the four fundamental subspaces and is presented in the context of vector spaces rather than as a computational recipe. This allows the authors to use linear independence, spanning sets and bases, and the four fundamental subspaces to explain and exploit Gaussian elimination and the LU factorization, as well as the solution of overdetermined linear systems in the least squares sense and eigenvalues and eigenvectors. This unique textbook also includes examples and problems focused on concepts rather than the mechanics of linear algebra. The problems at the end of each chapter and in an associated website encourage readers to explore how to use the notions introduced in the chapter in a variety of ways. Additional problems, quizzes, and exams will be posted on an accompanying website and updated regularly. The Less Is More Linear Algebra of Vector Spaces and Matrices is for students and researchers interested in learning linear algebra who have the mathematical maturity to appreciate abstract concepts that generalize intuitive ideas. The early introduction of the SVD makes the book particularly useful for those interested in using linear algebra in applications such as scientific computing and data science. It is appropriate for a first proof-based course in linear algebra.
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ISBN 10: 1611977398 ISBN 13: 9781611977394
Sprache: Englisch
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ISBN 10: 1611977398 ISBN 13: 9781611977394
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In den WarenkorbPaperback. Zustand: Brand New. 225 pages. 10.00x7.01x0.63 inches. In Stock.
Verlag: Springer, 2007
ISBN 10: 0387733930 ISBN 13: 9780387733937
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In den WarenkorbSoftcover. Zustand: Very Good. No Jacket. First edition. 1st 2007. Very good condition. Ten Lectures on Subjective Computing. Surveys and Tutorials in the Applied Mathematical Sciences Volume II. Yellow cardwraps. Some light creasing to covers. Contents are clean. Packaged with care and promptly dispatched!
Verlag: Society for Industrial and Applied Mathematics, 2013
ISBN 10: 1611972477 ISBN 13: 9781611972474
Sprache: Englisch
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ISBN 10: 1611977398 ISBN 13: 9781611977394
Sprache: Englisch
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ISBN 10: 1611977398 ISBN 13: 9781611977394
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ISBN 10: 1611977398 ISBN 13: 9781611977394
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ISBN 10: 1611972477 ISBN 13: 9781611972474
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paperback. Zustand: Good. Contains occasional marginalia and/or underlining. Light shelf wear. Else clean and tight.
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ISBN 10: 1611977398 ISBN 13: 9781611977394
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ISBN 10: 1611977398 ISBN 13: 9781611977394
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ISBN 10: 1611972477 ISBN 13: 9781611972474
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book of nature, according to Galilei, is written in the language of mat- matics. The nature of mathematics is being exact, and its exactness is und- lined by the formalism used by mathematicians to write it. This formalism, characterized by theorems and proofs, and syncopated with occasional l- mas, remarks and corollaries, is so deeply ingrained that mathematicians feel uncomfortable when the pattern is broken, to the point of giving the - pression that the attitude of mathematicians towards the way mathematics should be written is almost moralistic. There is a de nition often quoted, 'A mathematician is a person who proves theorems', and a similar, more alchemistic one, credited to Paul Erd os, but more likely going back to Alfr ed R enyi,statingthat'Amathematicianisamachinethattransformsco eeinto 1 theorems '. Therefore it seems to be the form, not the content, that char- terizes mathematics, similarly to what happens in any formal moralistic code wherein form takes precedence over content. This book is deliberately written in a very di erent manner, without a single theorem or proof. Since morality has its subjective component, to pa- phrase Manuel Vasquez Montalban, we could call it Ten Immoral Mathemat- 2 ical Recipes . Does the lack of theorems and proofs mean that the book is more inaccurate than traditional books of mathematics Or is it possibly just a sign of lack of co ee This is our rst open question. Exactness is an interesting concept.
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Taschenbuch. Zustand: Neu. An Introduction to Bayesian Scientific Computing | Ten Lectures on Subjective Computing | Daniela Calvetti (u. a.) | Taschenbuch | xiv | Englisch | 2007 | Springer New York | EAN 9780387733937 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Verlag: SIAM - Society for Industrial and Applied Mathematics, 2023
ISBN 10: 1611977398 ISBN 13: 9781611977394
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Verlag: Springer International Publishing AG, Cham, 2023
ISBN 10: 3031238230 ISBN 13: 9783031238239
Sprache: Englisch
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Hardcover. Zustand: new. Hardcover. The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. This book provides an insiders view of how to combine two mature fields, scientific computing and Bayesian inference, into a powerful language leveraging the capabilities of both components for computational efficiency, high resolution power and uncertainty quantification ability. The impact of Bayesian scientific computing has been particularly significant in the area of computational inverse problems where the data are often scarce or of low quality, but some characteristics of the unknown solution may be available a priori. The ability to combine the flexibility of the Bayesian probabilistic framework with efficient numerical methods has contributed to the popularity of Bayesian inversion, with the prior distribution being the counterpart of classical regularization. However, the interplay between Bayesian inference and numerical analysis is much richer than providing an alternative way to regularize inverse problems, as demonstrated by the discussion of time dependent problems, iterative methods, and sparsity promoting priors in this book. The quantification of uncertainty in computed solutions and model predictions is another area where Bayesian scientific computing plays a critical role. This book demonstrates that Bayesian inference and scientific computing have much more in common than what one may expect, and gradually builds a natural interface between these two areas. The once esoteric idea of embedding scientific computing into a probabilistic framework, mostly along the lines of the Bayesian paradigm, has recently enjoyed wide popularity and found its way into numerous applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.