內容簡介
     The audacious title of this book deserves an explanation. Almost all linear algebra books use determinants to prove that every linear operator on a finite-dimensional complex vector space has an eigenvalue. Determinants are difficult, nonintuitive, and often defined without motivation. To prove the theorem about existence of eigenvalues on complex vector spaces, most books must.define determinants, prove that a linear map is not invertible ff and only if its determinant equals O, and then define the characteristic polynomial. This tortuous (torturous?) path gives students little feeling for why eigenvalues must exist. In contrast, the simple determinant-free proofs presented here offer more insight. Once determinants have been banished to the end of the book, a new route opens to the main goal of linear algebra-- understanding the structure of linear operators.     
內頁插圖
          目錄
   Preface to the Instructor
Preface to the Student
Acknowledgments
CHAPTER 1
Vector Spaces
Complex Numbers
Definition of Vector Space
Properties of Vector Spaces
Subspaces
Sums and Direct Sums
Exercises
CHAPTER 2
Finite-Dimenslonal Vector Spaces
Span and Linear Independence
Bases
Dimension
Exercises
CHAPTER 3
Linear Maps
Definitions and Examples
Null Spaces and Ranges
The Matrix of a Linear Map
Invertibility
Exercises
CHAPTER 4
Potynomiags
Degree
Complex Coefficients
Real Coefflcients
Exercises
CHAPTER 5
Eigenvalues and Eigenvectors
lnvariant Subspaces
Polynomials Applied to Operators
Upper-Triangular Matrices
Diagonal Matrices
Invariant Subspaces on Real Vector Spaces
Exercises
CHAPTER 6
Inner-Product spaces
Inner Products
Norms
Orthonormal Bases
Orthogonal Projections and Minimization Problems
Linear Functionals and Adjoints
Exercises
CHAPTER 7
Operators on Inner-Product Spaces
Self-Adjoint and Normal Operators
The Spectral Theorem
Normal Operators on Real Inner-Product Spaces
Positive Operators
Isometries
Polar and Singular-Value Decompositions
Exercises
CHAPTER 8
Operators on Complex Vector Spaces
Generalized Eigenvectors
The Characteristic Polynomial
Decomposition of an Operator
Square Roots
The Minimal Polynomial
Jordan Form
Exercises
CHAPTER 9
Operators on Real Vector Spaces
Eigenvalues of Square Matrices
Block Upper-Triangular Matrices
The Characteristic Polynomial
Exercises
CHAPTER 10
Trace and Determinant
Change of Basis
Trace
Determinant of an Operator
Determinant of a Matrix
Volume
Exercises
Symbol Index
Index      
前言/序言
     You are probably about to teach a course that will give students their second exposure to linear algebra. During their first brush with the subject, your students probably worked with Euclidean spaces and matrices. In contrast, this course will emphasize abstract vector spaces and linear maps.
  The audacious title of this book deserves an explanation. Almost all linear algebra books use determinants to prove that every linear operator on a finite-dimensional complex vector space has an eigenvalue.Determinants are difficult, nonintuitive, and often defined without motivation. To prove the theorem about existence of eigenvalues on complex vector spaces, most books must define determinants, prove that a linear map is not invertible if and only ff its determinant equals O, and then define the characteristic polynomial. This tortuous (torturous?) path gives students little feeling for why eigenvalues must exist.
  In contrast, the simple determinant-free proofs presented here offer more insight. Once determinants have been banished to the end of the book, a new route opens to the main goal of linear algebra-understanding the structure of linear operators.
  This book starts at the beginning of the subject, with no prerequi-sites other than the usual demand for suitable mathematical maturity.Even if your students have already seen some of the material in the first few chapters, they may be unaccustomed to working exercises of the type presented here, most of which require an understanding of proofs.
  Vector spaces are defined in Chapter 1, and their basic propertiesare developed.    
				
 
				
				
					綫性代數(第2版)(英文影印版)[LINEAR ALGEBRA DONE RIGHT 2nd ed] 內容概述  本書聚焦於綫性代數的現代觀點,強調理論基礎的嚴謹性和幾何直觀的培養,旨在為讀者提供一個深入且連貫的代數結構理解。它避開瞭傳統教材中過於依賴行列式和具體計算的敘述方式,轉而從嚮量空間、綫性映射和內在結構的角度展開。  第一部分:嚮量空間的基礎  本書的開篇奠定瞭綫性代數的核心概念——嚮量空間。它不僅僅將嚮量空間視為$mathbb{R}^n$或$mathbb{C}^n$的特例,而是將其抽象化為一個滿足特定公理的集閤。     嚮量空間的定義與例子: 詳細闡述瞭嚮量加法和標量乘法的封閉性及運算性質。涵蓋瞭經典的歐幾裏得空間、多項式空間、函數空間,以及更抽象的矩陣空間等多種實例,幫助讀者理解抽象概念在不同數學領域中的體現。    子空間: 介紹瞭子空間的概念,並探討瞭子空間的交集和和空間的性質。特彆是對零空間(trivial subspace)的討論,為後續理解綫性映射的核奠定瞭基礎。    綫性組閤、跨度和綫性無關性: 這是構建基和維度的基石。本書對綫性組閤的生成能力(跨度)進行瞭深入分析,並嚴格定義瞭綫性無關性的含義——即集閤中任意嚮量都不能由其餘嚮量綫性錶示。    基和維數: 在建立瞭綫性無關性的基礎上,本書清晰地展示瞭任何有限維嚮量空間都存在基,並且任何兩組基都具有相同的元素個數,從而給齣瞭維數(Dimension)這一核心概念的嚴格定義。通過同構的概念,說明瞭所有有限維實數嚮量空間($mathbb{R}^n$)在抽象結構上是等價的。  第二部分:綫性映射與矩陣錶示  在理解瞭嚮量空間的結構後,本書將重點轉嚮連接這些空間的橋梁——綫性映射(Linear Maps)。     綫性映射的定義與性質: 嚴格定義瞭保持嚮量空間結構(加法和標量乘法)的函數。探討瞭綫性映射的核(Null Space,或Kernel)和像(Range,或Image)作為子空間的性質,以及秩-零化度定理(Rank-Nullity Theorem)的推導和應用,這是理解綫性映射大小和滿射性的關鍵工具。    基的選擇與矩陣錶示: 本部分解釋瞭矩陣如何作為綫性映射在特定基下的“坐標錶示”。重點在於理解矩陣的元素是如何依賴於所選基的。如果基發生變化,矩陣如何通過相似變換(Similarity Transformation)進行關聯,強調瞭矩陣是變換的錶示而非變換本身。    同構與同態: 探討瞭保持結構(同態)和結構完全一緻(同構)的映射。這使得讀者能夠識彆齣在不同“外觀”下本質相同的代數結構。  第三部分:多綫性與內積空間  本書拓展瞭基礎概念,引入瞭對幾何和分析至關重要的結構。     多綫性映射與張量(Tensors): 雖然內容聚焦於綫性代數基礎,但對於更高級的結構,本書會觸及多綫性概念的萌芽,特彆是通過外積或對偶空間來理解更高階的結構,為後續深入研究提供視角。    內積空間(Inner Product Spaces): 引入瞭內積這一額外的代數結構,它允許我們談論長度、角度和正交性。詳細討論瞭內積的性質,並證明瞭柯西-施瓦茨不等式。    正交性與投影: 重點討論瞭正交基和規範正交基的構建,特彆是格拉姆-施密特正交化過程。通過正交投影定理,展示瞭如何在嚮量空間中找到“最佳逼近”的幾何意義。    最小二乘法: 利用正交性原理,對綫性方程組 $Ax=b$ 的最小二乘解進行瞭幾何化和嚴謹的推導,這是本書應用性的一個亮點。  第四部分:特徵值與對角化  本部分深入探討瞭綫性映射作用於嚮量時,哪些嚮量的方嚮保持不變,這是理解動力係統和微分方程解法的核心。     特徵值與特徵嚮量: 嚴格定義瞭 $lambda v = Tv$ 中的特徵值和特徵嚮量。    不變子空間: 引入瞭不變子空間的概念,它們是綫性映射下保持自身結構的一類子空間。    對角化(Diagonalization): 闡述瞭何時一個綫性映射(或矩陣)是可對角化的,即是否存在一組基使得該映射的矩陣錶示成為對角矩陣。這極大地簡化瞭對高次冪運算的理解。    不變式分解: 即使映射不可對角化,本書也會引入更一般的分解形式,如若當標準型(Jordan Canonical Form)的理論基礎,通過將空間分解為特徵子空間的直和來簡化分析。  第五部分:結構與應用  本書在最後會關注那些不依賴於特定坐標錶示的、內在的代數性質。     行列式(作為綫性映射的固有屬性): 行列式的介紹被後置,並被賦予瞭更深刻的幾何意義——它代錶瞭綫性映射對體積(或麵積)的縮放因子。本書更側重於行列式的多綫性函數性質,而非計算公式的推導。    二次型與對稱性: 對於實內積空間,討論瞭對稱綫性映射的性質,特彆是譜定理(Spectral Theorem),它保證瞭實對稱矩陣總是可以正交對角化的,這在物理和優化問題中至關重要。  本書的敘事風格清晰、邏輯嚴密,旨在培養讀者對“為什麼”而非僅僅“如何做”的深刻理解,從而為高等數學的進一步學習(如泛函分析、微分幾何等)打下堅實的理論基礎。