GEKKO TypeError in python

Question

suppose :

# ww is a numpy array
ww.shape
>>>(10, 1)

# C is a numpy array
C.shape
>>>(5, 10)

i want to solve a optimization problem in python with specific objective function.
Here is the code that i wrote for that purpose:

from gekko import GEKKO

m = GEKKO()
x1 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x2 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x3 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x4 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2
x5 = m.Var(value=0.2, lb=0, ub=1, integer=False) #float variable. Lower bound = 0, Upper Bound = 1, inirial Value = 0.2

x = [x1, x2, x3, x4, x5]

# My subjective function
m.Equation(x1 + x2 + x3 + x4 + x5 == 1)

# My specific Objective Function
## Remember that I specified about ww and C arrays right upside of these Codes
def Objective(x):
    i = 0
    j = 0
    C_b = np.zeros((1,C.shape[1])) # so C_b.shape would be (1, 10)
    
    for i in range(C.shape[1]):
        for j in range(5):
            C_b[0][i] += math.log10(x[j] * C[j,i])
    
    
    return -sum((C_b * ww)[0])


m.Obj(Objective(x))
m.solve(disp=False)

print(x1.value, x2.value, x3.value, x4.value, x5.value)

Output:

TypeError: must be real number, not GK_Operators

Picture of Error:

i guess this error is cause of specific objective function! because with simple objective functions like :

m.Obj(x1 + x2)

I don't get error! so I guess the error comes from specific objective function.

How can I fix this error? where is the problem?

Answer 1

The Error Fixed by changing the shape of ww.
before fixing problem :

ww.shape
>>>(10, 1)

fixed The problem with :

ww.shape
>>>(10, )

Now proposed algorithm worked without any kind of error or problem. That mean it was cause of shape of ww! it fixed after I changed the shape of ww to (10, ) instead (10, 1) .

now Suppose :

# ww is a numpy array
ww.shape
>>>(10, )

# C is a numpy array
C.shape
>>>(5, 10)

Corrected & Proposed Algorithm :

from gekko import GEKKO
import numpy as np

nd = 5

m = GEKKO()
x = m.Array(m.Var,nd,value=1/nd,lb=0,ub=1)
m.Equation(sum(x)==1)

i = 0
j = 0
for i in range(C.shape[1]):
    for j in range(C.shape[0]):
        m.Maximize(ww[i]*(m.log10(x[j] *C[j,i])))
        
m.solve(disp=True)
for i,xi in enumerate(x):
    print(i+1,xi.value)