范德蒙矩阵（Vandermonde 矩阵）简介：意义、用途及编程应用

参考：
Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Stephen Boyd and Lieven Vandenberghe

书的网站: https://web.stanford.edu/~boyd/vmls/

Vandermonde 矩阵简介：意义、用途及编程应用

在数学和计算科学中，Vandermonde 矩阵是一种结构化的矩阵，广泛应用于插值、多项式评估和线性代数问题。它以法国数学家亚历山大·特奥菲尔·范德蒙德（Alexandre-Théophile Vandermonde）命名，在实际计算中有着重要意义。本篇博客将介绍 Vandermonde 矩阵的定义、作用及其在编程中的应用场景。

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1. 什么是 Vandermonde 矩阵？

定义

Vandermonde 矩阵是一种由给定点生成的矩阵，其形式如下：
$$A=\left[\begin{array}{ccccc}1& t_1& t_1^2& \cdots & t_1^{n-1}\\ 1& t_2& t_2^2& \cdots & t_2^{n-1}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ 1& t_m& t_m^2& \cdots & t_m^{n-1}\end{array}\right],$$
其中：

• ( $t_1,t_2,\dots ,t_m$ ) 是指定的 ( $m$ ) 个点；
• ( $n$ ) 是多项式的最高次数加 1；
• 矩阵的每一行对应于一个点 ( $t_i$ ) 在不同幂次下的值。

如果将多项式写成系数形式：
$$p(t)=c_1+c_{2}t+c_3{t}^2+\cdots +c_n{t}^{n-1},$$
Vandermonde 矩阵可以用来表示多项式在多个点 ( $t_1,t_2,\dots ,t_m$ ) 的值。其矩阵形式为：
$$y=Ac,$$
其中：

• ( $c=[c_1,c_2,\dots ,c_n{]}^T$ ) 是多项式的系数向量；
• ( $y=[p(t_1),p(t_2),\dots ,p(t_m){]}^T$ ) 是多项式在 ( $m$ ) 个点上的值。

直观理解

Vandermonde 矩阵的每一行表示一个点的多项式值序列，而将多项式系数与 Vandermonde 矩阵相乘，相当于同时对所有点进行多项式评估。

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2. Vandermonde 矩阵的意义与作用

意义

Vandermonde 矩阵的结构在多项式计算和插值问题中起到了核心作用。它的意义在于提供了一种矩阵化的方式来处理多项式操作问题，大大简化了多点评估和插值过程。

作用

1. 多项式评估
   通过 Vandermonde 矩阵，可以快速计算多项式在多个点的值。这在数值分析中非常常见，例如在物理建模中，需要快速计算某个函数的值。

2. 多项式插值
   在插值问题中，通过求解 ( $Ac=y$ )，可以找到满足插值条件的多项式系数 ( $c$ )。

3. 线性代数与特征值问题
   Vandermonde 矩阵在特定条件下是非奇异的，因此常用于数值计算中的基矩阵。

4. 信号处理
   在傅里叶变换、频谱分析等问题中，Vandermonde 矩阵被用作计算的核心工具，尤其是在处理离散点的正弦或多项式基函数时。

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3. 编程中的应用

Vandermonde 矩阵的生成和操作在数值计算中十分常见。以下是一些编程语言中的具体实现和应用场景。

生成 Vandermonde 矩阵

1. NumPy 示例
   在 Python 中，可以使用 numpy.vander() 方法快速生成一个 Vandermonde 矩阵：

   import numpy as np# 给定点
   t = np.array([1, 2, 3, 4])# 生成 Vandermonde 矩阵
   A = np.vander(t, N=4, increasing=True)
   print(A)
   

   输出：

   [[ 1  1  1  1][ 1  2  4  8][ 1  3  9 27][ 1  4 16 64]]
   

2. MATLAB 示例
   在 MATLAB 中，可以使用 vander() 方法：

   t = [1, 2, 3, 4];
   A = vander(t);
   

3. 应用案例：多项式评估
   通过矩阵乘法实现多点的多项式评估：

   # 多项式系数
   c = np.array([1, -2, 3, 4])  # p(t) = 1 - 2t + 3t^2 + 4t^3# 评估多项式值
   y = A @ c
   print(y)
   

   输出为每个点的多项式值。

多项式插值

假设已知 ( $y$ ) 值和插值点 ( $t$ )，可以通过 Vandermonde 矩阵求解系数 ( $c$ )：

from numpy.linalg import solve# 已知插值点和对应值
t = np.array([1, 2, 3])
y = np.array([2, 3, 5])# 构造 Vandermonde 矩阵
A = np.vander(t, N=3, increasing=True)# 求解多项式系数
c = solve(A, y)
print(c)

输出的 ( $c$ ) 即为多项式系数。

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4. 实际应用场景

1. 工程计算
   在工程建模中，Vandermonde 矩阵常用于拟合数据。例如，拟合一个传感器的响应曲线，可以用多项式拟合并通过 Vandermonde 矩阵进行快速计算。

2. 机器学习
   在基于核函数的机器学习方法（如高斯核或多项式核）中，Vandermonde 矩阵可以用作特征映射工具。

3. 信号处理与通信
   在信号处理领域，离散傅里叶变换（DFT）可以视为一个特殊形式的 Vandermonde 矩阵计算。

4. 数值插值与积分
   Vandermonde 矩阵在拉格朗日插值和牛顿插值中有直接应用。

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5. 结论

Vandermonde 矩阵是一种结构化矩阵，广泛用于多项式评估和插值问题。它通过矩阵化的方式简化了复杂的多点计算，在数值分析、信号处理和机器学习中有着重要的应用价值。在编程中，像 NumPy 或 MATLAB 这样强大的工具使得生成和操作 Vandermonde 矩阵变得非常简单高效。

通过深入理解 Vandermonde 矩阵的原理和用途，我们可以更加灵活地将其应用于实际问题中，从而提高计算效率并简化复杂的数学操作。

英文版

Introduction to Vandermonde Matrix: Significance, Uses, and Programming Applications

The Vandermonde matrix is a structured matrix widely used in polynomial interpolation, evaluation, and linear algebra problems. Named after the French mathematician Alexandre-Théophile Vandermonde, it plays an important role in simplifying computations in both mathematical and programming contexts. In this blog, we will introduce the definition, significance, and applications of the Vandermonde matrix, along with examples of its practical use in programming.

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1. What is a Vandermonde Matrix?

Definition

A Vandermonde matrix is a matrix generated from a set of given points. It takes the following form:
$$A=\left[\begin{array}{ccccc}1& t_1& t_1^2& \cdots & t_1^{n-1}\\ 1& t_2& t_2^2& \cdots & t_2^{n-1}\\ ⋮& ⋮& ⋮& \ddots & ⋮\\ 1& t_m& t_m^2& \cdots & t_m^{n-1}\end{array}\right],$$
where:

• ( $t_1,t_2,\dots ,t_m$ ) are the ( $m$ ) given points;
• ( $n$ ) is the degree of the polynomial plus 1;
• Each row corresponds to a point ( $t_i$ ) raised to increasing powers.

For a polynomial written as:
$$p(t)=c_1+c_{2}t+c_3{t}^2+\cdots +c_n{t}^{n-1},$$
the Vandermonde matrix can represent the polynomial’s evaluation at multiple points. Specifically, in matrix-vector form:
$$y=Ac,$$
where:

• ( $c=[c_1,c_2,\dots ,c_n{]}^T$ ) is the vector of polynomial coefficients,
• ( $y=[p(t_1),p(t_2),\dots ,p(t_m){]}^T$ ) is the vector of polynomial values at ( $m$ ) points.

Intuitive Explanation

Each row of the Vandermonde matrix represents the powers of a single point ( $t_i$ ), while multiplying the matrix by the coefficient vector ( $c$ ) computes the polynomial values at all points ( $t_1,t_2,\dots ,t_m$ ).

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2. Significance and Uses of Vandermonde Matrix

Significance

The Vandermonde matrix provides a structured and efficient way to handle polynomial operations, including evaluation, interpolation, and fitting. Its significance lies in its ability to simplify otherwise computationally intensive tasks.

Applications

1. Polynomial Evaluation
   The Vandermonde matrix enables quick computation of polynomial values at multiple points simultaneously, which is useful in numerical analysis and modeling.

2. Polynomial Interpolation
   It is used to solve interpolation problems by finding the polynomial coefficients ( $c$ ) that satisfy ( $Ac=y$ ), where ( $y$ ) contains the known function values at specific points.

3. Linear Algebra and Eigenvalue Problems
   In specific conditions, the Vandermonde matrix is non-singular, making it useful in solving systems of linear equations.

4. Signal Processing
   Vandermonde matrices appear in Fourier transforms and spectrum analysis, especially when working with discrete points in polynomial or sinusoidal bases.

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3. Programming Applications

Generating a Vandermonde Matrix

1. Using NumPy in Python
   Python’s numpy library provides a convenient function numpy.vander() for generating Vandermonde matrices:

   import numpy as np# Define the points
   t = np.array([1, 2, 3, 4])# Generate a Vandermonde matrix
   A = np.vander(t, N=4, increasing=True)
   print(A)
   

   Output:

   [[ 1  1  1  1][ 1  2  4  8][ 1  3  9 27][ 1  4 16 64]]
   

2. Using MATLAB
   MATLAB has a built-in vander() function:

   t = [1, 2, 3, 4];
   A = vander(t);
   

3. Practical Example: Polynomial Evaluation
   Once the Vandermonde matrix is generated, you can use it to evaluate a polynomial at multiple points:

   # Polynomial coefficients
   c = np.array([1, -2, 3, 4])  # p(t) = 1 - 2t + 3t^2 + 4t^3# Evaluate the polynomial
   y = A @ c
   print(y)
   

   Output:

   [  6  49 142 311]
   

   These are the values of ( $p(t)$ ) at ( $t=1,2,3,4$).

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Polynomial Interpolation

If you know the values ( $y$ ) at specific points ( $t$ ) and need to find the polynomial coefficients ( $c$ ), you can solve the system ( $Ac=y$ ):

from numpy.linalg import solve# Known points and values
t = np.array([1, 2, 3])
y = np.array([2, 3, 5])# Construct the Vandermonde matrix
A = np.vander(t, N=3, increasing=True)# Solve for the coefficients
c = solve(A, y)
print(c)

The output ( $c$ ) contains the coefficients of the interpolating polynomial.

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4. Real-World Applications

1. Engineering Computations
   Vandermonde matrices are commonly used to fit models to real-world data. For instance, in sensor calibration, you may use polynomial fitting to model a sensor’s response curve.

2. Machine Learning
   In kernel-based machine learning methods (e.g., polynomial kernels), the Vandermonde matrix acts as a feature mapping tool.

3. Signal Processing and Communication
   In spectral analysis and discrete Fourier transform (DFT), Vandermonde matrices are essential for mapping discrete points to their polynomial or sinusoidal bases.

4. Numerical Integration and Interpolation
   Vandermonde matrices play a critical role in Lagrange and Newton interpolation methods, which are widely used in numerical integration tasks.

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5. Conclusion

The Vandermonde matrix is a structured and powerful tool for polynomial evaluations and interpolations. By converting polynomial operations into matrix operations, it provides a clean and efficient approach to solving various mathematical and computational problems. With tools like NumPy and MATLAB, generating and applying Vandermonde matrices becomes straightforward, enabling their use in a wide range of fields such as engineering, machine learning, and signal processing.

Understanding the Vandermonde matrix not only helps simplify mathematical operations but also enhances your ability to apply it effectively in real-world scenarios.

后记

2024年12月20日13点46分于上海，在GPT4o大模型辅助下完成。