Scipy.optimize: how to restrict argument values

Question

I\'m trying to use scipy.optimize functions to find a global minimum of a complicated function with several arguments. scipy.optimize.minimize seem

Answer 1

The argument you are looking for is: constraints which is one of the arguments passed to scipy.minimize. Roll your own lambda function that receives the parameters to constrain like this:

#A function to define the space where scipy.minimize should 
#confine its search:
def apply_sum_constraint(inputs):
    #return value must come back as 0 to be accepted
    #if return value is anything other than 0 it's rejected
    #as not a valid answer.
    total = 50.0 - np.sum(inputs)
    return total

my_constraints = ({'type': 'eq', "fun": apply_sum_constraint })
result = spo.minimize(f, 
                      guess, 
                      method='SLSQP', 
                      args=(a, b, c),
                      bounds=((-1.0, 1.0), (-1.0, 1.0)),
                      options={'disp': True},
                      constraints=my_constraints)

The above example asserts that all the new candidates in the neighborhood of the last searched item better add up to 50. Change that method to define the permissible search space and the scipy.minimize function will waste no energy considering those answers.