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)
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