Python ValueError:在CVXPY最小化函数中设置具有序列的数组元素

我试图用cvxpy解决一个凸问题，如下所示

        import cvxpy as cp
        import numpy as np

        # Problem data.
        Q = np.array([[13, 12, -2], [12, 17, 6], [-2, 6, 12]])
        q = np.array([[-22, -14.5, 13]])
        r = 1

        # Construct the problem.
        x = cp.Variable((3,1))
        objective = cp.Minimize(np.dot(np.dot(x.T, Q), x) + np.dot(q, x) + r)

        constraints = [0 <= x[0:], x[0:] <= 1]
        prob = cp.Problem(objective, constraints)

        # The optimal objective value is returned by `prob.solve()`.
        result = prob.solve()
        # The optimal value for x is stored in `x.value`.
        print(x.value)
        # The optimal Lagrange multiplier for a constraint is stored in
        # `constraint.dual_value`.
        print(constraints[0].dual_value)

我不知道为什么会发生这个错误，因为其他一切似乎都正常

编辑：如果需要问题陈述，请告诉我。

请参阅上面的评论：

import cvxpy as cp
import numpy as np

# Problem data.
Q = np.array([[13, 12, -2], [12, 17, 6], [-2, 6, 12]])
q = np.array([[-22, -14.5, 13]])
r = 1

# Construct the problem.
x = cp.Variable((3,1))

# WE CAN'T USE NUMPY'S DOT
# ALSO: WE WANT TO EXPRESS AS MUCH STRUCTURE AS POSSIBLE -> cp.quad_form()!
# q*x is cvxpy-compatible expression -> quite algebraic compared to numpy
# -------------------------------------------------------------------------
objective = cp.Minimize(cp.quad_form(x, Q) + q*x + r)

# ORIGINAL SLICING IS A NO-OP
# ---------------------------
constraints = [0 <= x, x <= 1]

prob = cp.Problem(objective, constraints)

        # The optimal objective value is returned by `prob.solve()`.
result = prob.solve()
        # The optimal value for x is stored in `x.value`.
print(x.value)
        # The optimal Lagrange multiplier for a constraint is stored in
        # `constraint.dual_value`.
print(constraints[0].dual_value)

---

错误不完整，stacktrace甚至可能显示有问题的行。此外，您可能在复制粘贴该代码时出错，或者您的cvxpy版本很旧，因为

cp.Variable（3,1）

不应该这样做。但是，您通常的问题是，您使用cvxpy的方式不正确：cvxpy提供了原子函数，并重载了一些运算符，因此它可以在内部生成一些表达式图。在传递给cvxpy的表达式中使用非cvxpy内容是不允许的

np.dot

不应该在那里。原因1:cvxpy不知道如何处理它（它是外部的东西）原因2:np.dot（）是一个不太代数的表达式，而cvxpy允许像

a*x

（mat-vec-mul）这样的代数表达式，例如numpy需要点的地方。理由3：我想你想要cvxpy的

quad\u表单

表达式。@sascha谢谢你的评论。我遗漏了x=cp.Variable（3,1）周围的另一对括号。此后，我添加了它们以进行适当的调试。此外，我将研究您提供调试的原因。

import cvxpy as cp
import numpy as np

# Problem data.
Q = np.array([[13, 12, -2], [12, 17, 6], [-2, 6, 12]])
q = np.array([[-22, -14.5, 13]])
r = 1

# Construct the problem.
x = cp.Variable((3,1))

# WE CAN'T USE NUMPY'S DOT
# ALSO: WE WANT TO EXPRESS AS MUCH STRUCTURE AS POSSIBLE -> cp.quad_form()!
# q*x is cvxpy-compatible expression -> quite algebraic compared to numpy
# -------------------------------------------------------------------------
objective = cp.Minimize(cp.quad_form(x, Q) + q*x + r)

# ORIGINAL SLICING IS A NO-OP
# ---------------------------
constraints = [0 <= x, x <= 1]

prob = cp.Problem(objective, constraints)

        # The optimal objective value is returned by `prob.solve()`.
result = prob.solve()
        # The optimal value for x is stored in `x.value`.
print(x.value)
        # The optimal Lagrange multiplier for a constraint is stored in
        # `constraint.dual_value`.
print(constraints[0].dual_value)

[[ 8.46153846e-01]
[-6.34467676e-25]
[-1.92032635e-25]]
[[0.        ]
[5.80769231]
[9.61538462]]