Restrict scipy.optimize.minimize to integer values

Question

I\'m using scipy.optimize.minimize to optimize a real-world problem for which the answers can only be integers. My current code looks like this:

Answer 1

Here is a way to solve the Mixed Integer Nonlinear Programming problem with Python Gekko (a package that I maintain):

from gekko import GEKKO

m = GEKKO(remote=False)
x = m.Array(m.Var,9,lb=0,ub=7,integer=True)

def f(x):
    return (481.79/(5+x[0]))+(412.04/(4+x[1]))\
           +(365.54/(3+x[2]))+(375.88/(3+x[3]))\
           +(379.75/(3+x[4]))+(632.92/(5+x[5]))\
           +(127.89/(1+x[6]))+(835.71/(6+x[7]))\
           +(200.21/(1+x[8]))

m.Minimize(f(x))
m.Equation(sum(x)==7)
m.options.SOLVER=1
m.solve()
print(x)

This gives the solution:

 ---------------------------------------------------
 Solver         :  APOPT (v1.0)
 Solution time  :  0.0529 sec
 Objective      :  859.5269999999999
 Successful solution
 ---------------------------------------------------


[[0.0] [0.0] [1.0] [1.0] [1.0] [0.0] [1.0] [0.0] [3.0]]