So I’ve been trying to implement Jacobi’s method for solving systems of linear equations in python, and I’ve developed my code based on something like the following:

```
from numpy import array, zeros, diag, diagflat, dot
def jacobi(A,b,N=25,x=None):
"""Solves the equation Ax=b via the Jacobi iterative method."""
# Create an initial guess if needed
if x is None:
x = zeros(len(A[0]))
# Create a vector of the diagonal elements of A
# and subtract them from A
D = diag(A)
R = A - diagflat(D)
# Iterate for N times
for i in range(N):
x = (b - dot(R,x)) / D
return x
A = array([[2.0,1.0],[5.0,7.0]])
b = array([11.0,13.0])
guess = array([1.0,1.0])
sol = jacobi(A,b,N=25,x=guess)
```

But, when I try to develop a solution by taking user’s input for matrix size and elements as floats one by one, like:

```
dim = int(input(""))
A = np.zeros((dim,dim))
b = np.zeros((dim,1))
x = np.zeros((dim))
for i in range(dim):
for j in range(dim):
A[i,j] = float(input(""))
for i in range(dim):
b[i,0] = float(input(""))
```

I usually get the error:

only size-1 arrays can be converted to Python scalars

when defining my function as:

```
def Jacobi(A, B, x, n):
D = np.diag(A)
R = A - np.diagflat(D)
for i in range(n):
x = (B - np.dot(R,x))/ D
return x
```

I believe the problem might reside in the x update in the for loop and I’ve tried to find some manipulation inside numpy to fix it, but no success so far. I would appreciate any kind of help. I’m sorry if this doesn’t follow some guideline, I’m kind of new here.

Source: Python Questions