Challenge: Solving the Task Using SLE
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We have already considered how to solve the SLE using inversed matrix. But we can also use np.linalg.solve(A, y) method that calculates the solution of the SLE:
A * x = y.
Your task is to solve the system using both these methods and compare the results:
- Use
np.linalg.solve()method. - Use
np.inv()method to calculate inversed matrix and provide solution using:x = A_inv @ y.
Lösning
import numpy as np
# Define the coefficient matrix A and the constant vector B
A = np.array([[2, 3, -1], [4, -1, 5], [10, 2, 6]])
y = np.array([10, 4, 1])
# Solve the SLE using `np.linalg.solve()`
x = np.linalg.solve(A, y)
print(f'Solution using np.linalg.solve(): {x}')
# Calculate the inverse of matrix A
A_inv = np.linalg.inv(A)
# Solve the SLE using the inverse matrix method
x_inv = A_inv @ y
print(f'Solution using inverse matrix method: {x_inv}')
Var allt tydligt?
Tack för dina kommentarer!
Avsnitt 2. Kapitel 8
import numpy as np
# Define the coefficient matrix A and the constant vector B
A = np.array([[2, 3, -1], [4, -1, 5], [10, 2, 6]])
y = np.array([10, 4, 1])
# Solve the SLE using `np.linalg.solve()`
x = np.linalg.___(___, ___)
print(f'Solution using np.linalg.solve(): {x}')
# Calculate the inverse of matrix A
A_inv = np.linalg.___(___)
# Solve the SLE using the inverse matrix method
x_inv = A_inv ___ y
print(f'Solution using inverse matrix method: {x_inv}')
