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＜電子ブック＞
Linear Algebra with Python : Theory and Applications / by Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi
(Springer Undergraduate Texts in Mathematics and Technology. ISSN:18675514)

版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XV, 309 p. 91 illus., 64 illus. in color : online resource
著者標目	*Tsukada, Makoto author Kobayashi, Yuji author Kaneko, Hiroshi author Takahasi, Sin-Ei author Shirayanagi, Kiyoshi author Noguchi, Masato author SpringerLink (Online service)
件　名	LCSH:Algebras, Linear LCSH:Functional analysis LCSH:Python (Computer program language) FREE:Linear Algebra FREE:Functional Analysis FREE:Python
一般注記	Mathematics and Python -- Linear Spaces and Linear Mappings -- Basis and Dimension -- Matrices -- Elementary Operations and Matrix Invariants -- Inner Product and Fourier Expansion -- Eigenvalues and Eigenvectors -- Jordan Normal Form and Spectrum -- Dynamical Systems -- Applications and Development of Linear Algebra This textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms. A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron–Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python’s libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi HTTP:URL=https://doi.org/10.1007/978-981-99-2951-1

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