Linear algebra with Python : theory and applications / Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi.
Tsukada, Makoto, 1952-; Kobayashi, Yuji.; Kaneko, Hiroshi. 1933-; Takahasi, Sin-Ei.; Shirayanagi, Kiyoshi.; Noguchi, Masato.
2023
QA185.D37
Available Online

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Title Linear algebra with Python : theory and applications / Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi.
Author Tsukada, Makoto, 1952-
ISBN 9789819929511 (electronic bk.)
9819929512 (electronic bk.)
9789819929504
9819929504
DOI https://doi.org/10.1007/978-981-99-2951-1
Publication Details Singapore : Springer, 2023.
Language English
Description 1 online resource (315 p.).
Item Number 10.1007/978-981-99-2951-1 doi
Call Number QA185.D37
Dewey Decimal Classification 512.502855133
Summary 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 PerronFrobenius 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 Pythons 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.
Bibliography, etc. Note Includes bibliographical references and indexes.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed December 18, 2023).
Added Author Kobayashi, Yuji.
Kaneko, Hiroshi. 1933-
Takahasi, Sin-Ei.
Shirayanagi, Kiyoshi.
Noguchi, Masato.
Series Springer undergraduate texts in mathematics and technology.
Available in Other Form Linear Algebra with Python
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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.

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