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Titlebook: Linear Algebra with Python; Theory and Applicati Makoto Tsukada,Yuji Kobayashi,Masato Noguchi Textbook 2023 The Editor(s) (if applicable) a

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發(fā)表于 2025-3-21 19:28:40 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Linear Algebra with Python
副標題Theory and Applicati
編輯Makoto Tsukada,Yuji Kobayashi,Masato Noguchi
視頻videohttp://file.papertrans.cn/587/586284/586284.mp4
概述Gives a unified overview of various phenomena with linear structure from the perspective of functional analysis.Makes it enjoyable to learn linear algebra with Python by performing linear calculations
叢書名稱Springer Undergraduate Texts in Mathematics and Technology
圖書封面Titlebook: Linear Algebra with Python; Theory and Applicati Makoto Tsukada,Yuji Kobayashi,Masato Noguchi Textbook 2023 The Editor(s) (if applicable) a
描述.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 n
出版日期Textbook 2023
關(guān)鍵詞Orthogonal Projection; Fourier Expansion; Generalized Inverse; Singular Value Decomposition; Tensor Prod
版次1
doihttps://doi.org/10.1007/978-981-99-2951-1
isbn_softcover978-981-99-2953-5
isbn_ebook978-981-99-2951-1Series ISSN 1867-5506 Series E-ISSN 1867-5514
issn_series 1867-5506
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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978-981-99-2953-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Basis and Dimension,e notions of “basis” and “dimension”, and they turn out to be useful tools to analyze a linear space. Some important theorems concerning them will appear. The notion of linear independence is very important, and the readers are encouraged to understand its meaning to proceed to the next step.
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Mathematics and Python,In this chapter, we survey the notions of propositions, real and complex numbers, sets, and mappings (functions) that are the basics of mathematics needed to study linear algebra. We will also learn how these concepts are expressed in Python. Let us learn mathematics in a practical way by using Python.
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Matrices,In this chapter, we will learn the matrix representation of a linear mapping, and matrix operations defined naturally through representation.
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Elementary Operations and Matrix Invariants,In the previous chapter, we learned the matrix representation . of a linear mapping ..
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