线性代数第五版（美）吉尔伯特斯特朗（Gilbert Strang）著 2019年版

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资料名称：	线性代数第五版（美）吉尔伯特斯特朗（Gilbert Strang）著 2019年版
文件类型：	PDF文档
文件大小：	202.54 MB
资料归类：	数学书籍
整理时间：	2024-04-19
软件简介：	线性代数第五版作者：（美）吉尔伯特斯特朗（Gilbert Strang）著出版时间： 2019年版内容简介线性代数内容包括行列式、矩阵、线性方程组与向量、矩阵的特征值与特征向量、二次型及Mathematica 软件的应用等。每章都配有习题，书后给出了习题答案。本书在编写中力求重点突出、由浅入深、通俗易懂，努力体现教学的适用性。本书可作为高等院校工科专业的学生的教材，也可作为其他非数学类本科专业学生的教材或教学参考书。目录 Table of Contents 1 Introduction to Vectors 1 1.1 VectorsandLinearCombinations...................... 2 1.2 LengthsandDotProducts.......................... 11 1.3 Matrices ................................... 22 2 Solving Linear Equations 31 2.1 VectorsandLinearEquations........................ 31 2.2 TheIdeaofElimination........................... 46 2.3 EliminationUsingMatrices......................... 58 2.4 RulesforMatrixOperations ........................ 70 2.5 InverseMatrices............................... 83 2.6 Elimination = Factorization: A = LU .................. 97 2.7 TransposesandPermutations ........................ 108 3 Vector Spaces and Subspaces 122 3.1 SpacesofVectors .............................. 122 3.2 The Nullspace of A: Solving Ax = 0and Rx =0 ........... 134 3.3 The Complete Solution to Ax = b ..................... 149 3.4 Independence,BasisandDimension .................... 163 3.5 DimensionsoftheFourSubspaces ..................... 180 4 Orthogonality 193 4.1 OrthogonalityoftheFourSubspaces . . . . . . . . . . . . . . . . . . . . 193 4.2 Projections ................................. 205 4.3 LeastSquaresApproximations ....................... 218 4.4 OrthonormalBasesandGram-Schmidt. . . . . . . . . . . . . . . . . . . 232 5 Determinants 246 5.1 ThePropertiesofDeterminants....................... 246 5.2 PermutationsandCofactors......................... 257 5.3 Cramer’sRule,Inverses,andVolumes . . . . . . . . . . . . . . . . . . . 272 vii 6 Eigenvalues and Eigenvectors 287 6.1 IntroductiontoEigenvalues......................... 287 6.2 DiagonalizingaMatrix ........................... 303 6.3 SystemsofDifferentialEquations ..................... 318 6.4 SymmetricMatrices............................. 337 6.5 PositiveDe.niteMatrices.......................... 349 7 TheSingularValueDecomposition (SVD) 363 7.1 ImageProcessingbyLinearAlgebra .................... 363 7.2 BasesandMatricesintheSVD ....................... 370 7.3 Principal Component Analysis (PCA by the SVD) . . . . . . . . . . . . . 381 7.4 TheGeometryoftheSVD ......................... 391 8 LinearTransformations 400 8.1 TheIdeaofaLinearTransformation .................... 400 8.2 TheMatrixofaLinearTransformation. . . . . . . . . . . . . . . . . . . 410 8.3 TheSearchforaGoodBasis ........................ 420 9 ComplexVectorsand Matrices 429 9.1 ComplexNumbers ............................. 430 9.2 HermitianandUnitaryMatrices ...................... 437 9.3 TheFastFourierTransform......................... 444 10 Applications 451 10.1GraphsandNetworks ............................ 451 10.2MatricesinEngineering........................... 461 10.3 Markov Matrices, Population, and Economics . . . . . . . . . . . . . . . 473 10.4LinearProgramming ............................ 482 10.5 Fourier Series: Linear Algebra for Functions . . . . . . . . . . . . . . . . 489 10.6ComputerGraphics ............................. 495 10.7LinearAlgebraforCryptography...................... 501 11 NumericalLinear Algebra 507 11.1GaussianEliminationinPractice ...................... 507 11.2NormsandConditionNumbers....................... 517 11.3 IterativeMethodsandPreconditioners . . . . . . . . . . . . . . . . . . . 523 12LinearAlgebrain Probability& Statistics 534 12.1Mean,Variance,andProbability ...................... 534 12.2 Covariance Matrices and Joint Probabilities . . . . . . . . . . . . . . . . 545 12.3 Multivariate Gaussian and Weighted Least Squares . . . . . . . . . . . . 554 MatrixFactorizations 562 Index 564 SixGreatTheorems/LinearAlgebrain aNutshell 573
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