import numpy as np
def sor_solver(A, b, omega, initial_guess, convergence_criteria):
"""
This is an implementation of the pseudo-code provided in the Wikipedia article.
Arguments:
A: nxn numpy matrix.
b: n dimensional numpy vector.
omega: relaxation factor.
initial_guess: An initial solution guess for the solver to start with.
convergence_criteria: The maximum discrepancy acceptable to regard the current solution as fitting.
Returns:
phi: solution vector of dimension n.
"""
phi = initial_guess[:]
residual = np.linalg.norm(np.matmul(A, phi) - b) #Initial residual
while residual > convergence_criteria:
for i in range(A.shape[0]):
sigma = 0
for j in range(A.shape[1]):
if j != i:
sigma += A[i][j] * phi[j]
phi[i] = (1 - omega) * phi[i] + (omega / A[i][i]) * (b[i] - sigma)
residual = np.linalg.norm(np.matmul(A, phi) - b)
print('Residual: {0:10.6g}'.format(residual))
return phi
residual_convergence = 1e-8
omega = 0.5 #Relaxation factor
A = np.matrix([[4, -1, -6, 0],
[-5, -4, 10, 8],
[0, 9, 4, -2],
[1, 0, -7, 5]])
b = np.matrix([2, 21, -12, -6])
initial_guess = np.zeros(4)
phi = sor_solver(A, b, omega, initial_guess, residual_convergence)
print(phi)
I’m actually don’t know why this is don’t work… I have an error like:
Traceback (most recent call last):
File “sol.py”, line 42, in
phi = sor_solver(A, b, omega, initial_guess, residual_convergence)
File “sol.py”, line 22, in sor_solver
sigma += A[i][j] * phi[j]
File “C:\Users\1\AppData\Local\Programs\Python\Python38-32\lib\site-packages\numpy\matrixlib\defmatrix.py”, line 195, in getitem
out = N.ndarray.getitem(self, index)
IndexError: index 1 is out of bounds for axis 0 with size 1