[Python] how can I do a maximum likelihood regression using scipy.optimize.minimize

How can I do a maximum likelihood regression using scipy.optimize.minimize? I specifically want to use the minimize function here, because I have a complex model and need to add some constraints. I am currently trying a simple example using the following:

from scipy.optimize import minimize

def lik(parameters):
m = parameters[0]
b = parameters[1]
sigma = parameters[2]
for i in np.arange(0, len(x)):
y_exp = m * x + b
L = sum(np.log(sigma) + 0.5 * np.log(2 * np.pi) + (y - y_exp) ** 2 / (2 * sigma ** 2))
return L

x = [1,2,3,4,5]
y = [2,3,4,5,6]
lik_model = minimize(lik, np.array([1,1,1]), method='L-BFGS-B', options={'disp': True})


When I run this, convergence fails. Does anyone know what is wrong with my code?

The message I get running this is 'ABNORMAL_TERMINATION_IN_LNSRCH'. I am using the same algorithm that I have working using optim in R.

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