Introduction to Scientific Computing and Data Analysis: Texts in Computational Science and Engineering, cartea 13
Autor Mark H. Holmesen Limba Engleză Hardback – 12 iul 2023
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Springer International Publishing – 12 iul 2023 | 481.16 lei 38-44 zile |
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Specificații
ISBN-13: 9783031224294
ISBN-10: 3031224299
Pagini: 554
Ilustrații: XVI, 554 p. 201 illus., 178 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.93 kg
Ediția:Second Edition 2023
Editura: Springer International Publishing
Colecția Springer
Seria Texts in Computational Science and Engineering
Locul publicării:Cham, Switzerland
ISBN-10: 3031224299
Pagini: 554
Ilustrații: XVI, 554 p. 201 illus., 178 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.93 kg
Ediția:Second Edition 2023
Editura: Springer International Publishing
Colecția Springer
Seria Texts in Computational Science and Engineering
Locul publicării:Cham, Switzerland
Cuprins
Preface.- Preface to Second Edition.- Introduction to Scientific Computing.- Solving a Nonlinear Equation.- Matrix Equations.- Eigenvalue Problems.- Interpolation.- Numerical Integration.- Initial Value Problems.- Optimization: Regression.- Optimization: Descent Methods.- Data Analysis.- Taylor's Theorem.- Vector and Matrix Summary.- Answers.- References.- Index.
Notă biografică
Mark Holmes is a Professor at Rensselaer Polytechnic Institute. His current research interests include mechanoreception and sleep-wake cycles. Professor Holmes has three published books in Springer's Texts in Applied Mathematics series: Introduction to Perturbation Methods, Introduction to the Foundations of Applied Mathematics, and Introduction to Numerical Methods in Differential Equations.
Textul de pe ultima copertă
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.
Caracteristici
Codes used for all of the computational examples are available on GitHub Covers optimization methods, regression principal & independent component analysis & variational calculus Problem solving & the constructive use of the mathematical foundations of the subject
Recenzii
“The material is accompanied by many examples, exercises, as well as diagrams and tables of resulting data which are produced by MATLAB for which the programs are available on the author's website. The book is intended for beginners in scientific computing with basic knowledge in calculus, matrix algebra, and differential equations. … a good reading for the more advanced scientific engineer or lecturer in this field who may find numerous suggestions for working or teaching.” (Gudula Rünger, zbMATH 1368.65002, 2017)