在下面发表评论：

Question

仍在玩cvxpy。这次我收到一个有趣的错误。让我们看看这个最小的代码

import cvxpy as cp
import numpy as np

A = np.random.normal(0, 1, (64,6))
b = np.random.normal(0, 1, (64,1))
theta = cp.Variable(shape = (6,1))

prob = cp.Problem(
    cp.Minimize(cp.max(A*theta -b) <= 5),
    [-10 <= theta, theta <= 10])

一旦编译，我们就会收到错误

~\Anaconda3\lib\site-packages\cvxpy\expressions\constants\constant.py in __init__(self, value)
     42             self._sparse = True
     43         else:
---> 44             self._value = intf.DEFAULT_INTF.const_to_matrix(value)
     45             self._sparse = False
     46         self._imag = None

~\Anaconda3\lib\site-packages\cvxpy\interface\numpy_interface\ndarray_interface.py in const_to_matrix(self, value, convert_scalars)
     48             return result
     49         else:
---> 50             return result.astype(numpy.float64)
     51 
     52     # Return an identity matrix.

TypeError: float() argument must be a string or a number, not 'Inequality'

我很困惑，我必须说。

Answer 1

我不知道您想精确建模什么，但是这里有些有效：

import cvxpy as cp
import numpy as np

A = np.random.normal(0, 1, (64,6))
b = np.random.normal(0, 1, (64,1))
theta = cp.Variable(shape = (6,1))

prob = cp.Problem(
            cp.Minimize(cp.sum(theta)),  # what do you want to minimize?
            [
                cp.max(A*theta -b) <= 5,
                -10 <= theta,
                theta <= 10
            ]
        )

有效并且应该显示问题。

我更喜欢像这样的干净提示

import cvxpy as cp
import numpy as np

A = np.random.normal(0, 1, (64,6))
b = np.random.normal(0, 1, (64,1))
theta = cp.Variable(shape = (6,1))

obj = cp.Minimize(cp.sum(theta))          # what do you want to minimize?
                                          # feasibility-problem? -> use hardcoded constant: cp.Minimize(0)
constraints = [
    cp.max(A*theta -b) <= 5,
    -10 <= theta,
    theta <= 10
]

prob = cp.Problem(obj, constraints)

原因：更容易读出确切的情况。

您的问题：您的目标受到约束，这是不可能的。

import cvxpy as cp
import numpy as np

A = np.random.normal(0, 1, (64,6))
b = np.random.normal(0, 1, (64,1))
theta = cp.Variable(shape = (6,1))

prob = cp.Problem(
cp.Minimize(cp.max(A*theta -b) <= 5),  # first argument = objective
                                       # -> minimize (constraint) : impossible!
    [-10 <= theta, theta <= 10])       # second argument = constraints
                                       # -> box-constraints

简而言之：

您想要以最小化功能
您要做使不平等最小化

在下面发表评论：

编辑

obj = cp.Minimize(cp.max(cp.abs(A*theta-b)))

小支票：

print((A*theta-b).shape)
(64, 1)
print((cp.abs(A*theta-b)).shape)
(64, 1)

元素级Abs：好

最后的外部max产生单个值，否则cp.Minimize将不接受它。好

编辑或让我们让cvxpy变得更快乐：

obj = cp.Minimize(cp.norm(A*theta-b, "inf"))

cvxpy中的TypeEror：float（）参数必须是字符串或数字，而不是'Inequality'

1 个答案:

在下面发表评论：