Elementary Linear Algebra, 8/e (Metric Edition)(IE-Paperback)
暫譯: 初等線性代數，第8版（公制版）（IE-平裝本）

Ron Larson

Elementary Linear Algebra, 8/e (Metric Edition)(IE-Paperback)

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商品描述

ELEMENTARY LINEAR ALGEBRA, 8E, INTERNATIONAL METRIC EDITION's clear, careful, and concise presentation of material helps you fully understand how mathematics works. The author balances theory with examples, applications, and geometric intuition for a complete, step-by-step learning system. To engage you in the material, a new design highlights the relevance of the mathematics and makes the book easier to read. Data and applications reflect current statistics and examples, demonstrating the link between theory and practice. The companion website LarsonLinearAlgebra.com offers free access to multiple study tools and resources. CalcChat.com offers free step-by-step solutions to the odd-numbered exercises in the text. 

商品描述(中文翻譯)

《初等線性代數，第8版，國際公制版》清晰、謹慎且簡潔的材料呈現方式幫助您充分理解數學的運作。作者在理論、範例、應用和幾何直覺之間取得平衡，提供一個完整的逐步學習系統。為了讓您更投入於材料中，新的設計突顯了數學的相關性，並使書籍更易於閱讀。數據和應用反映了當前的統計數據和範例，展示了理論與實踐之間的聯繫。伴隨網站 LarsonLinearAlgebra.com 提供多種學習工具和資源的免費訪問。CalcChat.com 提供文本中奇數練習題的免費逐步解答。

目錄大綱

1. SYSTEMS OF LINEAR EQUATIONS.
Introduction to Systems of Equations. Gaussian Elimination and Gauss-Jordan Elimination. Applications of Systems of Linear Equations.
2. MATRICES.
Operations with Matrices. Properties of Matrix Operations. The Inverse of a Matrix. Elementary Matrices. Markov Chains. Applications of Matrix Operations.
3. DETERMINANTS.
The Determinant of a Matrix. Evaluation of a Determinant Using Elementary Operations. Properties of Determinants. Applications of Determinants.
4. VECTOR SPACES.
Vectors in Rn. Vector Spaces. Subspaces of Vector Spaces. Spanning Sets and Linear Independence. Basis and Dimension. Rank of a Matrix and Systems of Linear Equations. Coordinates and Change of Basis. Applications of Vector Spaces.
5. INNER PRODUCT SPACES.
Length and Dot Product in Rn. Inner Product Spaces. Orthogonal Bases: Gram-Schmidt Process. Mathematical Models and Least Squares Analysis. Applications of Inner Product Spaces.
6. LINEAR TRANSFORMATIONS.
Introduction to Linear Transformations. The Kernel and Range of a Linear Transformation. Matrices for Linear Transformations. Transition Matrices and Similarity. Applications of Linear Transformations.
7. EIGENVALUES AND EIGENVECTORS.
Eigenvalues and Eigenvectors. Diagonalization. Symmetric Matrices and Orthogonal Diagonalization. Applications of Eigenvalues and Eigenvectors.
8. COMPLEX VECTOR SPACES (online).
Complex Numbers. Conjugates and Division of Complex Numbers. Polar Form and Demoivre’s Theorem. Complex Vector Spaces and Inner Products. Unitary and Hermitian Spaces.
9. LINEAR PROGRAMMING (online).
Systems of Linear Inequalities. Linear Programming Involving Two Variables. The Simplex Method: Maximization. The Simplex Method: Minimization. The Simplex Method: Mixed Constraints.
10. NUMERICAL METHODS (online).
Gaussian Elimination with Partial Pivoting. Iterative Methods for Solving Linear Systems. Power Method for Approximating Eigenvalues. Applications of Numerical Methods.

目錄大綱(中文翻譯)

1. SYSTEMS OF LINEAR EQUATIONS.

Introduction to Systems of Equations. Gaussian Elimination and Gauss-Jordan Elimination. Applications of Systems of Linear Equations.

2. MATRICES.

Operations with Matrices. Properties of Matrix Operations. The Inverse of a Matrix. Elementary Matrices. Markov Chains. Applications of Matrix Operations.

3. DETERMINANTS.

The Determinant of a Matrix. Evaluation of a Determinant Using Elementary Operations. Properties of Determinants. Applications of Determinants.

4. VECTOR SPACES.

Vectors in Rn. Vector Spaces. Subspaces of Vector Spaces. Spanning Sets and Linear Independence. Basis and Dimension. Rank of a Matrix and Systems of Linear Equations. Coordinates and Change of Basis. Applications of Vector Spaces.

5. INNER PRODUCT SPACES.

Length and Dot Product in Rn. Inner Product Spaces. Orthogonal Bases: Gram-Schmidt Process. Mathematical Models and Least Squares Analysis. Applications of Inner Product Spaces.

6. LINEAR TRANSFORMATIONS.

Introduction to Linear Transformations. The Kernel and Range of a Linear Transformation. Matrices for Linear Transformations. Transition Matrices and Similarity. Applications of Linear Transformations.

7. EIGENVALUES AND EIGENVECTORS.

Eigenvalues and Eigenvectors. Diagonalization. Symmetric Matrices and Orthogonal Diagonalization. Applications of Eigenvalues and Eigenvectors.

8. COMPLEX VECTOR SPACES (online).

Complex Numbers. Conjugates and Division of Complex Numbers. Polar Form and Demoivre’s Theorem. Complex Vector Spaces and Inner Products. Unitary and Hermitian Spaces.

9. LINEAR PROGRAMMING (online).

Systems of Linear Inequalities. Linear Programming Involving Two Variables. The Simplex Method: Maximization. The Simplex Method: Minimization. The Simplex Method: Mixed Constraints.

10. NUMERICAL METHODS (online).

Gaussian Elimination with Partial Pivoting. Iterative Methods for Solving Linear Systems. Power Method for Approximating Eigenvalues. Applications of Numerical Methods.

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