Problem Analysis
With scipyPython function in the library minimizecan be solved
from scipy import optimize as opt
import numpy as np
from scipy.optimize import minimize
# https://blog.csdn.net/weixin_42262245/article/details/89310006
# 目标函数
def objective(x):
return x[0] ** 2 + x[1]**2 + x[2]**2 +8
# 约束条件
def constraint1(x):
return x[0] ** 2 - x[1] + x[2]**2 # 不等约束
def constraint2(x):
return -(x[0] + x[1]**2 + x[2]**2-20) # 不等约束
def constraint3(x):
return -x[0] - x[1]**2 + 2
def constraint4(x):
return x[1] + 2*x[2]**2 -3 # 不等约束
# 边界约束
b = (0.0, None)
bnds = (b, b ,b)
con1 = {'type': 'ineq', 'fun': constraint1}
con2 = {'type': 'ineq', 'fun': constraint2}
con3 = {'type': 'eq', 'fun': constraint3}
con4 = {'type': 'eq', 'fun': constraint4}
cons = ([con1, con2, con3,con4]) # 3个约束条件
x0=np.array([0, 0, 0])
# 计算
solution = minimize(objective, x0, method='SLSQP', \
bounds=bnds, constraints=cons)
x = solution.x
print('目标值: ' + str(objective(x)))
print('答案为')
print('x1 = ' + str(x[0]))
print('x2 = ' + str(x[1]))
