000			a
999			_c33410 _d33410
008			241114b xxu||||| |||| 00| 0 eng d
020			_a9789819929504
082			_a512.502855133 _bTSU
100			_aTsukada, Makoto
245			_aLinear Algebra with Python : theory and applications
260			_bSpringer, _c2023 _aSingapore :
300			_axv, 308 p. ; _bill., _c27 cm.
365			_b4077.00 _c₹ _d01
504			_aIncludes bibliographical references and index.
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 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.
650			_aAlgebra, Linear
650			_aFourier Analysis
650			_aFunctional analysis and transforms
650			_aMathematics Applied
650			_aMaths for engineers
650			_aMaths for scientists
650			_aWavelets
700			_aKobayashi, Yuji
700			_aKaneko, Hiroshi
700			_aTakahasi, Sin-Ei
700			_aShirayanagi, Kiyoshi
700			_aNoguchi, Masato
942			_2ddc _cBK