一个方程组,等式左边为一个系数矩阵(4×4)和一个列向量相乘,系数矩阵第一行为0.001,2,0,1,第二行为-3,2.099999,6,2,第三行为5,-1,5,-1,第四行为2,1,0,2,列向量为x1,x2,x3,x4,x5,等式右边为一个列向量,从上到下为8,5.900001,5,1,使用LU分解和列主元高斯消去法求解方程组,用python代码实现
时间: 2023-10-06 15:06:42 浏览: 95
CG.zip_16阶hilbert方程_16阶hilbert矩阵_16阶系数矩阵_airock_deerqeo
首先,我们需要导入numpy库来进行矩阵运算。
```
import numpy as np
```
定义系数矩阵A和列向量B,以及初始化LU分解的L和U矩阵。
```
A = np.array([[0.001, 2, 0, 1],
[-3, 2.099999, 6, 2],
[5, -1, 5, -1],
[2, 1, 0, 2]])
B = np.array([[8], [5.900001], [5], [1]])
n = len(A)
L = np.zeros((n, n))
U = np.zeros((n, n))
```
接下来,我们使用列主元高斯消去法求解方程组。由于需要进行列主元高斯消去法,我们需要定义一个函数来交换矩阵的行。
```
def swap_rows(matrix, i, j):
temp = np.copy(matrix[i,:])
matrix[i,:] = matrix[j,:]
matrix[j,:] = temp
```
然后,我们需要对A矩阵进行列主元高斯消去法操作。具体步骤如下:
1. 遍历每一列,找到该列中绝对值最大的元素所在的行。
2. 如果该元素所在的行不是当前列的第一行,就交换该行和当前列的第一行。
3. 对当前列进行高斯消元操作,使下面的元素都变为0。
```
for j in range(n):
swap_index = j
max_value = abs(A[j,j])
for i in range(j+1, n):
if abs(A[i,j]) > max_value:
max_value = abs(A[i,j])
swap_index = i
if swap_index != j:
swap_rows(A, j, swap_index)
swap_rows(B, j, swap_index)
for i in range(j+1, n):
factor = A[i,j] / A[j,j]
A[i,j:] = A[i,j:] - factor * A[j,j:]
B[i] = B[i] - factor * B[j]
```
此时,A矩阵的下三角部分就是L矩阵,而A矩阵的上三角部分就是U矩阵。我们可以将其分别赋值给L和U。
```
for i in range(n):
L[i,i] = 1
for j in range(i):
L[i,j] = A[i,j]
U[j,i] = 0
for j in range(i, n):
U[i,j] = A[i,j]
```
接下来,我们需要解出Ly=B和Ux=y两个方程组。首先,我们解出Ly=B。
```
Y = np.zeros((n,1))
for i in range(n):
Y[i] = B[i] / L[i,i]
for j in range(i):
Y[i] = Y[i] - L[i,j] / L[i,i] * Y[j]
```
然后,我们解出Ux=Y。
```
X = np.zeros((n,1))
for i in range(n-1, -1, -1):
X[i] = Y[i] / U[i,i]
for j in range(i+1, n):
X[i] = X[i] - U[i,j] / U[i,i] * X[j]
```
最后,我们输出解向量X。
```
print(X)
```
完整代码如下:
```
import numpy as np
def swap_rows(matrix, i, j):
temp = np.copy(matrix[i,:])
matrix[i,:] = matrix[j,:]
matrix[j,:] = temp
A = np.array([[0.001, 2, 0, 1],
[-3, 2.099999, 6, 2],
[5, -1, 5, -1],
[2, 1, 0, 2]])
B = np.array([[8], [5.900001], [5], [1]])
n = len(A)
L = np.zeros((n, n))
U = np.zeros((n, n))
for j in range(n):
swap_index = j
max_value = abs(A[j,j])
for i in range(j+1, n):
if abs(A[i,j]) > max_value:
max_value = abs(A[i,j])
swap_index = i
if swap_index != j:
swap_rows(A, j, swap_index)
swap_rows(B, j, swap_index)
for i in range(j+1, n):
factor = A[i,j] / A[j,j]
A[i,j:] = A[i,j:] - factor * A[j,j:]
B[i] = B[i] - factor * B[j]
for i in range(n):
L[i,i] = 1
for j in range(i):
L[i,j] = A[i,j]
U[j,i] = 0
for j in range(i, n):
U[i,j] = A[i,j]
Y = np.zeros((n,1))
for i in range(n):
Y[i] = B[i] / L[i,i]
for j in range(i):
Y[i] = Y[i] - L[i,j] / L[i,i] * Y[j]
X = np.zeros((n,1))
for i in range(n-1, -1, -1):
X[i] = Y[i] / U[i,i]
for j in range(i+1, n):
X[i] = X[i] - U[i,j] / U[i,i] * X[j]
print(X)
```
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