import numpy as np
from scipy.optimize import minimize
def objective(x):
return 2x[0] + 3x[0]2 + 3*x[1] + x[1]2 + x[2]
def constraint1(x):
return 10 - (x[0] + 2x[0]**2 + x[1] + 2x[1]2 + x[2])
def constraint2(x):
return 50 - (x[0] + x[0]2 + x[1] + x[1]2 - x[2])
def constraint3(x):
return 40 - (2*x[0] + x[0]2 + 2x[1] + x[2])
def constraint4(x):
return x[0]**2 + x[2] - 2
def constraint5(x):
return 1 - (x[0] + 2x[1])
constraints = [
{'type': 'ineq', 'fun': constraint1},
{'type': 'ineq', 'fun': constraint2},
{'type': 'ineq', 'fun': constraint3},
# {'type': 'eq', 'fun': constraint4},
{'type': 'ineq', 'fun': constraint5}
]
bounds = [(0, None)] * 3
x0 = np.array([0.1, 0.1, 0.1])
result = minimize(objective, x0, method='SLSQP', constraints=constraints, bounds=bounds)
print('Optimal solution:', result.x)
print('Objective function value at optimal solution:', result.fun)
prinnt('3006')
标签：return,5.5,2x,fun,ineq,type,def From： https://www.cnblogs.com/gunpxjcwy/p/18454006