Paperback. Condition: new. Paperback. This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra. This textbook provides an introduction to numerical computing and its applications in science and engineering. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031224324

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Introduction to Scientific Computing and Data Analysis

Mark H Holmes

Published by Springer Verlag Gmbh Jul 2024, 2024

ISBN 10: 3031224329 ISBN 13: 9783031224324

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Introduction to Scientific Computing and Data Analysis

Mark H. Holmes

Published by Springer International Publishing, 2024

ISBN 10: 3031224329 ISBN 13: 9783031224324

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra. Seller Inventory # 9783031224324

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