LEADER 03877nam 22006255 450 001 9910770272303321 005 20251113173800.0 010 $a981-9929-51-2 024 7 $a10.1007/978-981-99-2951-1 035 $a(CKB)29310839500041 035 $a(MiAaPQ)EBC31001788 035 $a(Au-PeEL)EBL31001788 035 $a(DE-He213)978-981-99-2951-1 035 $a(EXLCZ)9929310839500041 100 $a20231206d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLinear Algebra with Python $eTheory and Applications /$fby Makoto Tsukada, Yuji Kobayashi, Hiroshi Kaneko, Sin-Ei Takahasi, Kiyoshi Shirayanagi, Masato Noguchi 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (315 pages) 225 1 $aSpringer Undergraduate Texts in Mathematics and Technology,$x1867-5514 311 08$a9789819929504 327 $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. 330 $aThis textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms. A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron?Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python?s libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi. 410 0$aSpringer Undergraduate Texts in Mathematics and Technology,$x1867-5514 606 $aAlgebras, Linear 606 $aFunctional analysis 606 $aPython (Computer program language) 606 $aLinear Algebra 606 $aFunctional Analysis 606 $aPython 615 0$aAlgebras, Linear. 615 0$aFunctional analysis. 615 0$aPython (Computer program language) 615 14$aLinear Algebra. 615 24$aFunctional Analysis. 615 24$aPython. 676 $a512.502855133 700 $aTsukada$b Makoto$01460749 701 $aKobayashi$b Yu?ji$01180805 701 $aKaneko$b Hiroshi$01460750 701 $aTakahasi$b Sin-Ei$01460751 701 $aShirayanagi$b Kiyoshi$01096973 701 $aNoguchi$b Masato$01460752 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910770272303321 996 $aLinear Algebra with Python$93660713 997 $aUNINA