问题描述
我的MWE如下
def obj(e, p):
S = f(e) + g(p)
return S
我想仅在e
上最小化此函数,并将p
作为参数传递给该函数.但是,我也想要一个依赖于p
和e
的约束,其形式为p + e < 1
I would like to minimize this function over only e
and pass p
as an argument to the function. However, I also would like a constraint that depends on p
and e
that is of the form p + e < 1
我尝试了
cons = {'type': 'ineq',
'fun': lambda e, p: -e -p + 1,
'args': (p)}
然后,对于p = 0.5
minimize(obj, initial_guess, method = 'SLSQP', args = 0.5, constraints = cons)
但这是行不通的.在定义cons
的行中出现错误name 'p' is not defined
.如何将参数p
传递给目标函数和约束?
but this doesn't work. I get the error name 'p' is not defined
in the line where I define cons
. How do I pass the argument p
to both the objective function and the constraint?
下面的完整代码
from scipy.optimize import minimize
from scipy.stats import entropy
import numpy as np
#Create a probability vector
def p_vector(x):
v = np.array([x, 1-x])
return v
#Write the objective function
def obj(e, p):
S = -1*entropy(p_vector(p + e), base = 2)
return S
##Constraints
cons = {'type': 'ineq',
'fun': lambda e: -p - e + 1,
'args': (p,)
}
initial_guess = 0
result = minimize(obj, initial_guess, method = 'SLSQP', args = (0.5, ), constraints = cons)
print(result)
推荐答案
好吧,我认为这是语法错误以及如何传递参数的混合体.对于可能有相同问题的人,我将在此处发布答案.
Okay, I figured that it's a mix of syntax errors on my part and how arguments should be passed. For those who may have the same question, I will post an answer here.
目标函数是obj(e, p)
.我们只想最小化e
,所以我们创建其他参数arguments = (0.5,)
的元组.即,设置特定值p=0.5
.接下来定义约束函数
The objective function is obj(e, p)
. We only want to minimize e
so we create a tuple of the other arguments arguments = (0.5,)
. That is, a specific value of p=0.5
is set. Next define the constraint function
def prob_bound(e, p):
return -e - p + 1
现在将约束字典写为
cons = ({'type': 'ineq',
'fun': prob_bound,
'args': arguments
})
最后,有人叫最小化器
result = minimize(obj, initial_guess, method = 'SLSQP', args = arguments, constraints = cons)
这篇关于Scipy最小化:如何将args传递给目标和约束的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!