scipy minimize with constraints

Question

I know that this question should be handled in the manual of scipy.optimize, but I don't understand it well enough. Maybe you can help

I have a function (this is just an example, not the real function, but I need to understand it at this level):

Edit (better example):

Let's suppose I have a matrix

arr = array([[0.8, 0.2],[-0.1, 0.14]]) 

with a target function

def matr_t(t):     return array([[t[0], 0],[t[2]+complex(0,1)*t[3], t[1]]]  def target(t):     arr2 = matr_t(t)     ret = 0     for i, v1 in enumerate(arr):           for j, v2 in enumerate(v1):                ret += abs(arr[i][j]-arr2[i][j])**2     return ret 

now I want to minimize this target function under the assumption that the t[i] are real numbers, and something like t[0]+t[1]=1

Answer 1

This constraint

t[0] + t[1] = 1 

would be an equality (type='eq') constraint, where you make a function that must equal zero:

def con(t):     return t[0] + t[1] - 1 

Then you make a dict of your constraint (list of dicts if more than one):

cons = {'type':'eq', 'fun': con} 

I've never tried it, but I believe that to keep t real, you could use:

con_real(t):     return np.sum(np.iscomplex(t)) 

And make your cons include both constraints:

cons = [{'type':'eq', 'fun': con},         {'type':'eq', 'fun': con_real}] 

Then you feed cons into minimize as:

scipy.optimize.minimize(func, x0, constraints=cons)