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001484778 019__ $$a1412643099$$a1413374546
001484778 020__ $$a9789819929511$$q(electronic bk.)
001484778 020__ $$a9819929512$$q(electronic bk.)
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001484778 0247_ $$a10.1007/978-981-99-2951-1$$2doi
001484778 035__ $$aSP(OCoLC)1414467497
001484778 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dOCLKB$$dYDX$$dOCLCO
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001484778 050_4 $$aQA185.D37
001484778 08204 $$a512.502855133
001484778 1001_ $$aTsukada, Makoto,$$d1952-
001484778 24510 $$aLinear algebra with Python :$$btheory and applications /$$cMakoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi.
001484778 260__ $$aSingapore :$$bSpringer,$$c2023.
001484778 300__ $$a1 online resource (315 p.).
001484778 336__ $$atext$$btxt$$2rdacontent
001484778 337__ $$acomputer$$bc$$2rdamedia
001484778 338__ $$aonline resource$$bcr$$2rdacarrier
001484778 4901_ $$aSpringer Undergraduate Texts in Mathematics and Technology
001484778 504__ $$aIncludes bibliographical references and indexes.
001484778 5050_ $$aMathematics and Python -- Linear Spaces and Linear Mappings -- Basis and Dimension -- Matrices -- Elementary Operations and Matrix Invariants -- Inner Product and Fourier Expansion -- Eigenvalues and Eigenvectors -- Jordan Normal Form and Spectrum -- Dynamical Systems -- Applications and Development of Linear Algebra.
001484778 506__ $$aAccess limited to authorized users.
001484778 520__ $$aThis textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms. A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the PerronFrobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Pythons libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi.
001484778 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 18, 2023).
001484778 650_6 $$aAlgèbre linéaire$$xInformatique.
001484778 650_6 $$aPython (Langage de programmation)
001484778 650_0 $$aAlgebras, Linear$$xData processing.$$0(DLC)sh 85003441
001484778 650_0 $$aPython (Computer program language)$$0(DLC)sh 96008834
001484778 655_0 $$aElectronic books.
001484778 7001_ $$aKobayashi, Yuji.
001484778 7001_ $$aKaneko, Hiroshi.$$d1933-$$0(OCoLC)oca00098956
001484778 7001_ $$aTakahasi, Sin-Ei.
001484778 7001_ $$aShirayanagi, Kiyoshi.
001484778 7001_ $$aNoguchi, Masato.
001484778 77608 $$iPrint version:$$aTsukada, Makoto$$tLinear Algebra with Python$$dSingapore : Springer,c2024$$z9789819929504
001484778 830_0 $$aSpringer undergraduate texts in mathematics and technology.
001484778 852__ $$bebk
001484778 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-2951-1$$zOnline Access$$91397441.1
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001484778 980__ $$aBIB
001484778 980__ $$aEBOOK
001484778 982__ $$aEbook
001484778 983__ $$aOnline
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