I would like to find the minimum of 3dvar function defined as:

J(x)=(x-x_b)B^{-1}(x-x_b)^T + (y-H(x)) R^{-1} (y-H(x))^T (latex code)

with B,H,R,x_b,y given. I would like to find the argmin(J(x)). However it seems fmin in python does not work. (the function J works correctly)

Here is my code:

```
import numpy as np
from scipy.optimize import fmin
import math
def dvar_3(x):
B=np.eye(5)
H=np.ones((3,5))
R=np.eye(3)
xb=np.ones(5)
Y=np.ones(3)
Y.shape=(Y.size,1)
xb.shape=(xb.size,1)
value=np.dot(np.dot(np.transpose(x-xb),(np.linalg.inv(B))),(x-xb)) +np.dot(np.dot(np.transpose(Y-np.dot(H,x)),(np.linalg.inv(R))),(Y-np.dot(H,x)))
return value[0][0]
ini=np.ones(5) #
ini.shape=(ini.size,1) #change initial to vertical vector
fmin(dvar_3,ini) #start at initial vector
```

I receive this error:

```
ValueError: operands could not be broadcast together with shapes (5,5) (3,3)
```

How can I solve this problem? Thank you in advance.

reshape argument

`x`

in the function`dvar_3`

, the`init`

argument of`fmin()`

needs a one-dim array.