內容簡介
is a thorough introduction to linear algebra,for the graduate or advanced undergraduate student。 Prerequisites are limited to a knowledge of the basic properties of matrices and determinants。 However,since we cover the basics of vector spaces and linear transformations rather rapidly,a prior course in linear algebra (even at the sophomore level),along with a certain measure of "mathematical maturity," is highly desirable。
內頁插圖
目錄
Preface to the Third Edition,vii
Preface to the Second Edition,ix
Preface to the First Edition,xi
Preliminaries
Part 1: Preliminaries
Part 2: Algebraic Structures
Part I-Basic Linear Algebra
1 Vector Spaces
Vector Spaces
Subspaces
Direct Sums
Spanning Sets and Linear Independence
The Dimension of a Vector Space
Ordered Bases and Coordinate Matrices
The Row and Column Spaces of a Matrix
The C0mplexification of a Real Vector Space
Exercises
2 Linear Transformations
Linear Transformations
The Kernel and Image of a Linear Transformation
Isomorphisms
The Rank Plus Nullity Theorem
Linear Transformations from Fn to Fm
Change of Basis Matrices
The Matrix of a Linear Transformation
Change of Bases for Linear Transformations
Equivalence of Matrices
Similarity of Matrices
Similarity of Operators
Invariant Subspaces and Reducing Pairs
Projection Operators
Topological Vector Spaces
Linear Operators on Vc
Exercises
3 The Isomorphism Theorems
Quotient Spaces
The Universal Property of Quotients and the First Isomorphism Theorem
Quotient Spaces,Complements and Codimension
Additional Isomorphism Theorems
Linear Functionals
Dual Bases
Reflexivity
Annihilators
Operator Adjoints
Exercises
4 Modules I: Basic Properties
Motivation
Modules
Submodules
Spanning Sets
Linear Independence
Torsion Elements
Annihilators
Free Modules
Homomorphisms
Quotient Modules
The Correspondence and Isomorphism Theorems
Direct Sums and Direct Summands
Modules Are Not as Nice as Vector Spaces
Exercises
5 Modules II: Free and Noetherian Modules
The Rank of a Free Module
Free Modules and Epimorphisms
Noetherian Modules
The Hilbert Basis Theorem
Exercises
6 Modules over a Principal Ideal Domain
Annihilators and Orders
Cyclic Modules
Free Modules over a Principal Ideal Domain
Torsion-Free and Free Modules
The Primary Cyclic Decomposition Theorem
The Invariant Factor Decomposition
Characterizing Cyclic Modules
lndecomposable Modules
Exercises
Indecomposable Modules
Exercises 159
7 The Structure of a Linear Operator
The Module Associated with a Linear Operator
The Primary Cyclic Decomposition of VT
The Characteristic Polynomial
Cyclic and Indecomposable Modules
The Big Picture
The Rational Canonical Form
Exercises
8 Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Geometric and Algebraic Multiplicities
The Jordan Canonical Form
Triangularizability and Schurs Theorem
Diagonalizable Operators
Exercises
9 Real and Complex Inner Product Spaces
Norm and Distance
Isometrics
Orthogonality
Orthogonal and Orthonormal Sets
The Projection Theorem and Best Approximations
The Riesz Representation Theorem
Exercises
10 Structure Theory for Normal Operators
The Adjoint of a Linear Operator
Orthogonal Projections
Unitary Diagonalizability
Normal Operators
Special Types of Normal Operators
Seif-Adjoint Operators
Unitary Operators and Isometries
The Structure of Normal Operators
Functional Calculus
Positive Operators
The Polar Decomposition of an Operator
Exercises
Part Ⅱ-Topics
11 Metric Vector Spaces: The Theory of Bilinear Forms
Symmetric Skew-Symmetric and Alternate Forms
The Matrix ofa Bilinear Form
Quadratic Forms
Orthogonality
Linear Functionals
Orthogonal Complements and Orthogonal Direct Sums
Isometrics
Hyperbolic Spaces
Nonsingular Completions ofa Subspace
The Witt Theorems: A Preview
The Classification Problem for Metric Vector Spaces
Symplectic Geometry
The Structure of Orthogonal Geometries: Orthogonal Bases
The Classification of Orthogonal Geometries:Canonical Forms
The Orthogonal Group
The Witt Theorems for Orthogonal Geometries
Maximal Hyperbolic Subspaces of an Orthogonal Geometry
Exercises
12 Metric Spaces
The Definition
Open and Closed Sets
Convergence in a Metric Space
The Closure of a Set
Dense Subsets
Continuity
Completeness
Isometrics
The Completion of a Metric Space
Exercises
13 Hilbert Spaces
A Brief Review
Hilbert Spaces
Infinite Series
An Approximation Problem
Hilbert Bases
Fourier Expansions
A Characterization of Hilbert Bases
Hilbert Dimension
A Characterization of Hilbert Spaces
The Riesz Representation Theorem
Exercises
14 Tensor Products
Universality
Bilinear Maps
Tensor Products
When Is a Tensor Product Zero?
Coordinate Matrices and Rank
Characterizing Vectors in a Tensor Product
Defining Linear Transformations on a Tensor Product
The Tensor Product of Linear Transformations
Change of Base Field
Multilinear Maps and Iterated Tensor Products
Tensor Spaces
Special Multilinear Maps
Graded Algebras
The Symmetric and Antisymmetric Tensor Algebras
The Determinant
Exercises
15 Positive Solutions to Linear Systems:Convexity and Separation
Convex Closed and Compact Sets
Convex Hulls
Linear and Affine Hyperplanes
Separation
Exercises
16 Affine Geometry
Affine Geometry
Affine Combinations
Affine Hulls
The Lattice of Flats
Affine Independence
Affine Transformations
Projective Geometry
Exercises
17 Singular Values and the Moore-Penrose Inverse
Singular Values
The Moore-Penrose Generalized Inverse
Least Squares Approximation
Exercises
18 An Introduction to Algebras
Motivation
Associative Algebras
Division Algebras
Exercises
19 The Umbral Calculus
Formal Power Series
The Umbral Algebra
Formal Power Series as Linear Operators
Sheffer Sequences
Examples of Sheffer Sequences
Umbral Operators and Umbral Shifts
Continuous Operators on the Umbral Algebra
Operator Adjoints
Umbral Operators and Automorphisms of the Umbral Algebra
Umbral Shifts and Derivations of the Umbral Algebra
The Transfer Formulas
A Final Remark
Exercises
References
Index of Symbols
Index
前言/序言
Let me begin by thanking the readers of the second edition for their many helpful comments and suggestions, with special thanks to Joe Kidd and Nam Trang. For the third edition, I have corrected all known errors, polished and refined some arguments (such as the discussion of reflexivity, the rational canonical form, best approximations and the definitions of tensor products) and upgraded some proofs that were originally done only for finite-dimensional/rank cases. I have also moved some of the material on projection operators to an earlier oosition in the text.
綫性代數基礎與進階:現代數學的基石 本書旨在為讀者提供一個全麵、深入且富有洞察力的綫性代數學習體驗。它不僅僅是一本教科書,更是一部引導讀者跨越初級概念、邁嚮抽象思維和應用實踐的智力之旅。 本書的結構設計經過精心考量,確保知識的連貫性和邏輯的嚴密性。我們深知,綫性代數是連接純數學、應用數學、工程學、計算機科學乃至經濟學等諸多學科的關鍵橋梁。因此,本書緻力於構建一個堅實的基礎,同時為更高級的主題做好充分準備。 第一部分:嚮量空間與綫性變換的基石 本書伊始,我們首先奠定最核心的概念——嚮量空間(Vector Spaces)。我們不滿足於對 $mathbb{R}^n$ 的簡單討論,而是將定義擴展到更一般的抽象嚮量空間,包括函數空間、矩陣空間等。這要求讀者從具體的幾何直覺過渡到抽象的代數結構。 綫性組閤、張成與綫性相關性(Linear Combinations, Span, and Linear Dependence): 這是理解子空間的基礎。我們通過大量的例子和反例來闡明這些概念在不同空間中的錶現形式,強調綫性無關性的重要性,它是構建基(Basis)的前提。 基與維度(Basis and Dimension): 維度作為嚮量空間大小的度量,被賦予瞭深刻的意義。本書詳細探討瞭如何找到任意嚮量空間的基,以及維度定理的嚴謹證明,例如 $dim(U+W) = dim(U) + dim(W) - dim(U cap W)$。 綫性變換(Linear Transformations): 綫性變換是抽象代數與幾何直觀之間的紐帶。我們用矩陣來錶示這些變換,並深入探討瞭變換的核(Null Space/Kernel)和像(Range/Image)。核描述瞭變換如何“壓縮”空間,像則描述瞭變換能“觸及”到的空間範圍。 第二部分:矩陣代數的結構與應用 矩陣是綫性代數的計算工具,但其背後蘊含著深刻的代數結構。本部分重點剖析矩陣的運算及其在解決實際問題中的威力。 矩陣乘法與逆矩陣(Matrix Multiplication and Inverses): 不僅展示如何進行乘法運算,更重要的是理解矩陣乘法代錶的復閤變換的幾何意義。逆矩陣的存在性與唯一性,以及求解綫性方程組 $Amathbf{x} = mathbf{b}$ 的穩定性分析,是本節的重點。 行列式(Determinants): 行列式被視為衡量綫性變換對麵積或體積的縮放因子。我們不僅介紹代數計算方法(代數餘子式展開),還深入探討其在體積解釋、可逆性判斷以及剋拉默法則(Cramer's Rule)中的應用。 矩陣的秩與列空間(Rank of a Matrix and Column Space): 通過行簡化(Row Reduction)這一核心算法,我們係統地確定矩陣的秩,並理解行空間、列空間和零空間之間的關係,這直接關係到綫性方程組解的存在性和唯一性。 第三部分:特徵值、特徵嚮量與對角化 特徵值理論是綫性代數中最具應用價值的部分之一,它揭示瞭綫性變換在特定方嚮上隻進行純粹拉伸或壓縮的本質。 特徵值與特徵嚮量的求解(Eigenvalues and Eigenvectors): 我們詳細闡述如何通過求解特徵方程 $det(A - lambda I) = 0$ 來找到特徵值,以及如何確定對應的特徵嚮量。 對角化(Diagonalization): 當一個 $n imes n$ 矩陣擁有 $n$ 個綫性無關的特徵嚮量時,它可以被對角化。本書展示瞭對角化在計算矩陣高次冪、求解動力係統(如馬爾可夫鏈)中的高效性。 不變子空間與特徵空間的分解(Invariant Subspaces and Eigenspace Decomposition): 我們探討瞭特徵空間是如何構成嚮量空間的直和分解,這為理解更復雜的相似變換(Similarity Transformations)打下瞭基礎。 第四部分:內積空間與正交性 將度量引入抽象的嚮量空間,即引入內積(Inner Product),使得我們可以談論長度、角度和投影。 內積、範數與正交性(Inner Product, Norm, and Orthogonality): 在一般的復數或實數嚮量空間中定義內積,從而導齣長度(範數)的概念。正交性,作為“最純粹的不相關”,在數據分析和信號處理中至關重要。 施密特正交化過程(Gram-Schmidt Orthonormalization): 這是一個構造性的過程,用於將任意基轉換為標準正交基。這個過程是理解傅裏葉分析和最小二乘法的基礎。 正交投影與最小二乘法(Orthogonal Projection and Least Squares): 當一個綫性係統無解時,我們追求“最佳近似解”。正交投影理論提供瞭嚴謹的數學框架來找到使誤差最小的解,這是數據擬閤和迴歸分析的核心。 對稱矩陣與譜定理(Symmetric Matrices and the Spectral Theorem): 對於實對稱矩陣,譜定理保證瞭它們可以被正交對角化。這在主成分分析(PCA)等降維技術中具有不可替代的地位。 第五部分:超越域的探討(進階主題概述) 本書在最後部分對更高級的代數結構進行瞭必要的展望,旨在為後續的抽象代數或高級應用課程做鋪墊。 Jordan 標準型(Jordan Canonical Form): 針對那些不可對角化的矩陣,Jordan 標準型提供瞭一種“最接近對角化”的規範形式,對於求解微分方程和研究矩陣函數的解析性至關重要。 多重綫性映射(Multilinear Maps)與張量初步: 介紹張量(Tensor)的基本概念,將其視為多重綫性函數,這是進入微分幾何、廣義相對論和高級物理學的必經之路。 本書的特點在於其對概念的深度挖掘和對證明的嚴格要求。我們認為,隻有理解瞭“為什麼”——即證明背後的邏輯——纔能真正掌握綫性代數。書中的例題豐富多樣,既有展示基礎運算的簡潔算例,也有需要深刻洞察纔能解決的難題,旨在全麵鍛煉讀者的數學建模和抽象推理能力。