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Linear algebra with Python :theory and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Linear algebra with Python :Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi.
Reminder of title:	theory and applications /
Author:	Tsukada, Makoto,
other author:	Kobayashi, Yuji.
Published:	Singapore :Springer,2023.
Description:	xv, 308 p. :ill. ;26 cm.
Subject:	Algebras, LinearData processing.
Online resource:	Click here to view book
Online resource:	https://doi.org/10.1007/978-981-99-2951-1
Online resource:	https://app.kortext.com/borrow/2509639
ISBN:	9789819929511

Linear algebra with Python :theory and applications /
Tsukada, Makoto,1952-

Linear algebra with Python :theory and applications /Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi. - Singapore :Springer,2023. - xv, 308 p. :ill. ;26 cm. - Springer Undergraduate Texts in Mathematics and Technology. - Springer undergraduate texts in mathematics and technology..

Includes bibliographical references and indexes.

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 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.

ISBN: 9789819929511

Standard No.: 10.1007/978-981-99-2951-1doiSubjects--Topical Terms:

185816
Algebras, Linear
--Data processing.

LC Class. No.: QA185.D37

Dewey Class. No.: 512.502855133

Linear algebra with Python :theory and applications /
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245 1 0 $a Linear algebra with Python : $b theory and applications / $c Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi.
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300 $a xv, 308 p. : $b ill. ; $c 26 cm.
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490 1 $a Springer Undergraduate Texts in Mathematics and Technology
504 $a Includes bibliographical references and indexes.
505 0 $a 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.
520 $a 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.
588 0 $a Online resource; title from PDF title page (SpringerLink, viewed December 18, 2023).
650 0 $a Algebras, Linear $x Data processing. $3 185816
650 0 $a Python (Computer program language) $3 215247
650 6 $a Algébre linéaire $x Informatique. $2 ram. $3 959572
650 6 $a Python (Langage de programmation) $3 943797
700 1 $a Kobayashi, Yuji. $3 959565
700 1 $a Kaneko, Hiroshi. $3 959566
700 1 $a Takahasi, Sin-Ei. $3 959567
700 1 $a Shirayanagi, Kiyoshi. $3 959568
700 1 $a Noguchi, Masato. $3 959569
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