本书是在作者多年来开设“数值分析”双语教学讲义的基础上，吸收国内外同类教材的精华，采用中英文双语编写而成。主要介绍了计算机中常用的、有效的各类数值问题的计算方法及相关数学理论。全书共分八章，包括误差分析、插值、函数逼近、数值积分和数值微分、非线性方程的数值解法、线性方程组的直接解法、线性方程组的迭代解法、常微分方程的数值解法等内容。每章由开篇、理论、应用和习题四个部分构成。

本书介绍了科学计算中常用数值分析的基础理论及计算机实现方法。主要内容包括：误差分析、插值、函数逼近、数值积分和数值微分、非线性方程的数值解法、线性方程组的直接解法、线性方程组的迭代解法、常微分方程的数值解法及相应的上机实验内容等。各章都配有大量的习题及上机实验题目，并附有部分习题的参考答案及数学专业软件Mathematica和Matlab的简介。

本书采用中、英两种语言编写，适合作为数学、计算机和其他理工类各专业本科“数值分析(计算方法)”双语课程的教材或参考用书，也可供从事科学计算的相关技术人员参考。

1 Error Analysis

1.1 Introduction

1.2 Sources of Errors

1.3 Errors and Significant Digits

1.4 Error Propagation

1.5 Qualitative Analysis and Control of Errors

  1.5.1 Ill-condition Problem and Condition Number

  1.5.2 The Stability of Algorithm

  1.5.3 The Control of Errors

1.6 Computer Experiments

  1.6.1 Functions Needed in The Experiments by Mathematica

  1.6.2 Experiments by Mathematica

  1.6.3 Functions Needed in The Experiments by Matlab

  1.6.4 Experiments by Matlab

2 Interpolating

2.1 Introduction

2.2 Basic Concepts

2.3 Lagrange Interpolation

  2.3.1 Linear and Parabolic Interpolation

  2.3.2 Lagrange Interpolation Polynomial

  2.3.3 Interpolation Remainder and Error Estimate

2.4 Divided-differences and Newton Interpolation

2.5 Differences and Newton Difference Formulae

  2.5.1 Differences

  2.5.2 Newton Difference Formulae

2.6 Hermite Interpolation

2.7 Piecewise Low Degree Interpolation

  2.7.1 Ill-posed Properties of High Degree Interpolation

  2.7.2 Piecewise Linear Interpolation

  2.7.3 Piecewise Interpolation of Hermite with Degree Three

2.8 Cubic Spline Interpolation

  2.8.1 Definition of Cubic Spline

  2.8.2 The Construction of Cubic Spline

2.9 Computer Experiments

  2.9.1 Functions Needed in The Experiments by Mathematica

  2.9.2 Experiments by Mathematica

3 Best Approximation

3.1 Introduction

3.2 Norms

  3.2.1 Vector Norms

  3.2.2 Matrix Norms

3.3 Spectral Radius

3.4 Best Linear Approximation

  3.4.1 Basic Concepts and Theories

  3.4.2 Best Linear Approximation

3.5 Discrete Least Squares Approximation

3.6 Least Squares Approximation and Orthogonal Polynomials

3.7 Computer Experiments

  3.7.1 Functions Needed in The Experiments by Mathematica

  3.7.2 Experiments by Mathematica

  3.7.3 Functions Needed in The Experiments by Matlab

  3.7.4 Experiments by Matlab

4 Numerical Integration and Differentiation

4.1 Introduction

4.2 Interpolatory Quadratures

  4.2.1 Interpolatory Quadratures

  4.2.2 Degree of Accuracy

4.3 Newton-Cotes Quadrature Formula

4.4 Composite Quadrature Formula

  4.4.1 Composite Trapezoidal Rule

  4.4.2 Composite Simpson's Rule

4.5 Romberg Integration

  4.5.1 Recursive Trapezoidal Rule

  4.5.2 Romberg Algorithm

  4.5.3 Richardson's Extrapolation

4.6 Gaussian Quadrature Formula

4.7 Numerical Differentiation

  4.7.1 Numerical Differentiation

  4.7.2 Differentiation Polynomial Interpolation

  4.7.3 Richardson's Extrapolation

4.8 Computer Experiments

  4.8.1 Functions Needed in The Experiments by Mathematica

  4.8.2 Experiments by Mathematica

  4.8.3 Experiments by Matlab

5 Solution of Nonlinear Equations

5.1 Introduction

5.2 Basic Theories

5.3 Bisection Method

5.4 Iterative Method and Its Convergence

  5.4.1 Fixed Point and Iteration

  5.4.2 Global Convergence

  5.4.3 Local Convergence

  5.4.4 Order of Convergence

5.5 Accelerating Convergence

5.6 Newton's Method

  5.6.1 Newton's Method and Its Convergence

  5.6.2 Reduced Newton Method and Newton's Descent Method

  5.6.3 The Case of Multiple Roots

5.7 Secant Method and Muller Method

  5.7.1 Secant Method

  5.7.2 Muller Method

5.8 Systems of Nonlinear Equations

5.9 Computer Experiments

  5.9.1 Functions Needed in The Experiments by Mathematica

  5.9.2 Experiments by Mathematica

  5.9.3 Experiments by Matlab

6 Direct Methods for Solving Linear Systems

6.1 Introduction

6.2 Gaussian Elimination

  6.2.1 Basic Gaussian Elimination

  6.2.2 Triangular Decomposition

6.3 Gaussian Elimination with Column Pivoting

6.4 Methods of The Triangular Decomposition

  6.4.1 The Direct Methods of The Triangular Decomposition

  6.4.2 The Square Root Method

  6.4.3 The Speedup Method

6.5 Analysis of Round-off Errors

  6.5.1 Condition Number

  6.5.2 Iterative Refinement

6.6 Computer Experiments

  6.6.1 Functions Needed in The Experiments by Mathematica

  6.6.2 Experiments by Mathematica

  6.6.3 Functions Needed in The Experiments by Matlab

  6.6.4 Experiments by Matlab

7 Iterative Techniques for Solving Linear Systems

7.1 Introduction

7.2 Basic Iterative Methods

  7.2.1 Jacobi Method

  7.2.2 Gauss-Seidel Method

  7.2.3 SOR Method .

7.3 Iterative Method Convergence

  7.3.1 Basic Theorems

  7.3.2 Some Special Systems of Equations

7.4 Computer Experiments

  7.4.1 Functions Needed in The Experiments by Mathematica

  7.4.2 Experiments by Mathematica

8 Numerical Solution of Ordinary Differential Equations

8.1 Introduction

8.2 The Existence and Uniqueness of Solutions

8.3 Taylor-Series Method

8.4 Euler's Method

8.5 Single-step Methods

  8.5.1 Single-step Methods

  8.5.2 Local Truncation Error

8.6 Runge-Kutta Methods

  8.6.1 Second-Order Runge-Kutta Method

  8.6.2 Fourth-Order Runge-Kutta Method

8.7 Multistep Methods

  8.7.1 General Formulas of Multistep Methods

  8.7.2 Adams Explicit and Implicit Formulas

8.8 Systems and Higher-Order Differential Equations

  8.8.1 Vector Notation

  8.8.2 Taylor-Series Method for Systems

  8.8.3 Fourth-Order Runge-Kutta Formula for Systems

8.9 Computer Experiments

  8.9.1 Functions Needed in The Experiments by Mathematica

  8.9.2 Experiments by Mathematica

附录

 附录A Mathematica基本操作

 附录B Matlab基本操作

 附录C Answers to Selected Problems

参考文献