Я пишу код для решения проблемы ниже. 
Но я получаю сообщение об ошибке в своем коде, в котором говорится:
TypeError: constraint3() missing 1 required positional argument: 'x'
Мой код:
import pandas as pd
import numpy as np
import scipy as sp
from scipy.optimize import minimize
import matplotlib.pyplot as plt
%matplotlib inline
step=80
f1load=[44,48,53,28,32,36,41,48,38,32,38,34,44,36,41,48,38,32,44,48,53,28,32,36,41,48,38,32,38,34,44,36,41,48,38,32,44,48,53,28,32,36,41,48,38,32,38,34,44,36,41,48,38,32,44,48,53,28,32,36,41,48,38,32,38,34,44,36,41,48,38,32,44,48,53,28,32,36,41,48,38,32,38,34,44,36,41,48]
fload=f1load[0:step+1]
i1load=[40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40]
iload=i1load[0:step+1]
load1=np.array(iload)-np.array(fload)
load1
load2=load1.tolist()
load=load2
x = np.zeros(80,dtype='i')
x = x.tolist()
def objective(x,load):
obj2 = 0
# obj1=sum(((load[i+1]-(6*x[i]))*(load[i+1]-(6*x[i])) for i in range (n)))
for i in range(step):
obj1 = (load[i+1]-(6*x[i]))*(load[i+1]-(6*x[i]))
obj2 = obj2 + obj1
return obj2
iSOC = 50 #initial state of charge
tSOC = 65 #final state of charge
def constraint1(y):
y1 = y[0]
return y1 - iSOC
def constraint2(y):
yend = y[step]
return yend - tSOC
def constraint3(y,x):
for i in range(step):
con3[i] = y[i+1] - y[i] - 3.75*x[i]
return con3
b = (-1.5,1.5)
bnds = (b,b,b,b,b,b,b,b,b,b,b,b,b,b,b,\
b,b,b,b,b,b,b,b,b,b,b,b,b,b,b,\
b,b,b,b,b,b,b,b,b,b,b,b,b,b,b,\
b,b,b,b,b,b,b,b,b,b,b,b,b,b,b,\
b,b,b,b,b,b,b,b,b,b,b,b,b,b,b,\
b,b,b,b,b)
con1 = {'type' : 'eq' , 'fun' : constraint1}
con3 = {'type' : 'eq' , 'fun' : constraint3}
con2 = {'type' : 'ineq' , 'fun' : constraint2}
cons = [con1,con3,con2]
sol = minimize(objective, x, method = 'SLSQP',\
bounds = bnds, constraints = cons)
Трассировка полного стека ошибки:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-64-b961b5feff89> in <module>
1 sol = minimize(objective, x, method = 'SLSQP',\
----> 2 bounds = bnds, constraints = cons)
~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
616 elif meth == 'slsqp':
617 return _minimize_slsqp(fun, x0, args, jac, bounds,
--> 618 constraints, callback=callback, **options)
619 elif meth == 'trust-constr':
620 return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,
~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/slsqp.py in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)
311 # meq, mieq: number of equality and inequality constraints
312 meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
--> 313 for c in cons['eq']]))
314 mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
315 for c in cons['ineq']]))
~/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/slsqp.py in <listcomp>(.0)
311 # meq, mieq: number of equality and inequality constraints
312 meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
--> 313 for c in cons['eq']]))
314 mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
315 for c in cons['ineq']]))
TypeError: constraint3() missing 1 required positional argument: 'x'