Im trying to get the global minimum of a non linear function with linear constraints. I share you the code:
rho = 1.0
g = 9.8
C = 1.0 #roughness coefficient
J = rho*g/(float(1e6)) #constant of function
capacity = [0.3875, 0.607, 0.374] #Supply data
demand = [0.768, 0.315, 0.38,] #Demand data
m= len(capacity)
n = len(demand)
x0 = np.array(m*n*[0.01]) #Initial point for algorithm
# In[59]:
#READ L AND d FROM ARCGIS !!!
L = (314376.57, 277097.9663, 253756.9869 ,265786.5632, 316712.6028, 232857.1468, 112063.9914, 135762.94, 131152.8206)
h= (75, 75, 75, 75, 75, 75, 1320,75,75)
K = np.zeros(m*n)
N=np.zeros(m*n)
# In[60]:
#Each path has its own L,d, therefore, own constant
# In[61]:
#np.seterr(all='ignore')
def diameter(x):
d = 1.484
if x >= 0.276:
d = 1.484
elif x >= 0.212:
d = 1.299
elif x >= 0.148:
d = 1.086
elif x >= 0.0975:
d = 0.881
elif x >= 0.079:
d = 0.793
elif x >= 0.062:
d = 0.705
elif x >= 0.049:
d = 0.626
elif x >= 0.038:
d = 0.555
elif x >= 0.030:
d = 0.494
elif x >= 0.024:
d = 0.441
elif x >= 0.0198:
d = 0.397
elif x >= 0.01565:
d = 0.353
elif x >= 0.0123:
d = 0.313
elif x >= 0.0097:
d = 0.278
elif x >= 0.0077:
d = 0.247
elif x >= 0.0061:
d = 0.22
elif x >= 0.0049:
d = 0.198
elif x >= 0.00389:
d = 0.176
elif x >= 0.0032:
d = 0.159
elif x >= 0.0025:
d = 0.141
elif x >= 0:
d = 0.123
return d
#Definition of Objetive function
def objective(x):
sum_obj = 0.0
for i in range(len(L)):
K = 10.674*L[i]/(C**1.852*diameter(x[i])**4.871)
N[i] = K*x[i]**2.852*J+x[i]*J*h[i]
sum_obj = sum_obj + N[i]
return sum_obj
print(str(objective(x0)))
#Definition of the constraints
GB=[]
for i in range(m):
GB.append(n*i*[0]+n*[1]+(m-1-i)*n*[0])
P=[]
for i in range(n):
P.append(m*(i*[0]+[1]+(n-1-i)*[0]))
DU=np.array(GB+P)
lb = np.array(m*[0] + demand) # Supply
ub = np.array(capacity + n*[np.inf]) # Demand
# In[62]:
b = (0, 1)
bnds = []
for i in range(m*n):
bnds.append(b)
cons = LinearConstraint(DU,lb,ub)
solution = differential_evolution(objective,x0,cons,bnds)
x = solution.x
# show initial objective
print('Initial SSE Objective: ' + str(objective(x0)))
# show final objective
print('Final SSE Objective: ' + str(objective(x)))
# print solution
print('Solution Supply to Demand:')
print('Q = ' + str(((np.around(x,6)).reshape(10,18))))
I dont know why, but when I run appear the following:
"if strategy in self._binomial: TypeError: unhashable type: 'list'
Anyone have gotten the same mistake ? Its my first time trying to solve optimization problem, so I need a little bit of help. Any advice is welcome !! Thanks a lot !
