Python Scipy Optimization.minimize使用SLSQP显示最大化结果 [英] Python Scipy Optimization.minimize using SLSQP showing maximized results
问题描述
我正在学习用scipy.optimize.minimize优化一个多变量约束非线性问题，但是收到了奇怪的结果.
I am learning to optimize a multivariate constrained nonlinear problem with scipy.optimize.minimize,but received strange results.
我的问题:
minimize objfun
objfun x*y
constraints 0<=x<=5, 0<=y<=5, x+y==5
我的代码:
from scipy import optimize
def func(x):
return x[0]*x[1]
bnds=((0,100),(0,5))
cons=({'type':'eq','fun':lambda x:x[0]+x[1]-5})
x0=[0,0]
res= optimize.minimize(func,x0,method='SLSQP',bounds=bnds,constraints=cons)
收到的结果:
status: 0
success: True
njev: 2
nfev: 8
fun: 6.2499999999999991
x: array([ 2.5, 2.5])
message: 'Optimization terminated successfully.'
jac: array([ 2.5, 2.5, 0. ])
nit: 2
status: 0
success: True
njev: 2
nfev: 8
fun: 6.2499999999999991
x: array([ 2.5, 2.5])
message: 'Optimization terminated successfully.'
jac: array([ 2.5, 2.5, 0. ])
nit: 2
我希望乐趣为0或显着接近0，x或y为0
I am expecting the fun to be 0 or significantly close to 0 and x or y to be 0
推荐答案
我认为您遇到了麻烦.如果您尝试使用非对称猜测，那么您将收敛到正确的解决方案.
I think you are hitting a edge case. If you try with a guess that is not symmetric, you converge to the right solution.
只需将x0=[0,0]更改为其他内容，例如x0=[.2,.9].
Just change x0=[0,0] to something else, like x0=[.2,.9].
在@pv评论后展开.
[x,y]=[2.5,2.5]是受约束函数的局部最大值.在跳到该局部最大值之后，算法会再次计算将目标最小化所应采取的方向.
[x,y]=[2.5,2.5] is a local maximum of the constrained function. After jumping to this local maximum, the algorithm calculates again the direction it should take to minimize the target.
它是通过计算[ 2.50000001 2.5 ]和[ 2.5 2.50000001]处的值来实现的.发现该方向为(-1,-1).但是，该方向与约束正交，然后停止.
It does so by calculating the value at [ 2.50000001 2.5 ] and at [ 2.5 2.50000001]. It finds that this direction is (-1,-1). This direction is however orthogonal to the constraint, and it then stops.
之所以出现问题，是因为目标和约束相对于x=y是对称的，并且我们是从x=y的确切猜测开始的.
The problem arises because the target and the constraint are symmetric with respect to x=y, and that we are starting with the guess exactly on x=y.
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