擅长:python、mysql、java
<p>我希望这能作为你的开场白。都是<a href="http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.minimize.html" rel="nofollow">there</a>。在</p>
<pre><code>import numpy as np
from scipy.optimize import minimize
def my_fun(z):
x = z[0]
y = z[1]
f = np.zeros(2)
f[0] = x + y ** 2
f[1] = np.exp(x) + x * y
return np.dot(f,f)
def my_cons(z):
x = z[0]
y = z[1]
f = np.zeros(4)
f[0] = x + 5.5
f[1] = 0.18 - x
f[2] = y - 2.1
f[3] = 10.6 - y
return f
cons = {'type' : 'ineq', 'fun': my_cons}
res = minimize(my_fun, (2, 0), method='SLSQP',\
constraints=cons)
res
</code></pre>
<p><code>status: 0
success: True
njev: 7
nfev: 29
fun: 14.514193585986144
x: array([-0.86901099, 2.1 ])
message: 'Optimization terminated successfully.'
jac: array([ -2.47001648e-04, 3.21871972e+01, 0.00000000e+00])
nit: 7</code></p>
<p>编辑:作为对注释的回应:如果您的函数值<code>f1</code>和<code>f2</code>不为零,您只需重写方程,例如:</p>
<p>f1=-6和f2=3</p>
<p>您要最小化的功能是:</p>
^{pr2}$