如何在 cyipopt 中将选项传递给 objective

Question

我正在尝试将选项传递给我在 cyipopt 中解决的 NLP。

这些选项会在每次迭代中以相同的方式影响 objective。比如教程的问题是最小化

x_1 * x_4 * (x_1 + x_2 + _3) + x_3

受某些限制（参见 https://pythonhosted.org/ipopt/tutorial.html）。

我想解决相关问题

规模* x_1 * x_4 * (x_1 + x_2 + _3) + x_3

其中比例是优化前设置的参数。下面的代码显示了如何在pyipopt中设置问题，但是比例硬编码为2。如何将其设置为选项以便灵活更改？

import ipopt
import numpy as np

class hs071(object):
    def __init__(self):
        pass

    def objective(self, x, scale):
        # The callback for calculating the objective
        scale = 2
        
        return scale * x[0] * x[3] * np.sum(x[0:3]) + x[2]

    def gradient(self, x, scale):
        # The callback for calculating the gradient
        scale = 2
        
        return np.array([
                    scale * x[0] * x[3] + scale * x[3] * np.sum(x[0:3]),
                    scale * x[0] * x[3],
                    scale * x[0] * x[3] + 1.0,
                    scale * x[0] * np.sum(x[0:3])
                    ])

    def constraints(self, x):
        # The callback for calculating the constraints
        return np.array((np.prod(x), np.dot(x, x)))

    def jacobian(self, x):
        # The callback for calculating the Jacobian
        return np.concatenate((np.prod(x) / x, 2*x))

x0 = [1.0, 5.0, 5.0, 1.0]

lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]

cl = [25.0, 40.0]
cu = [2.0e19, 40.0]

nlp = ipopt.problem(
            n=len(x0),
            m=len(cl),
            problem_obj=hs071(),
            lb=lb,
            ub=ub,
            cl=cl,
            cu=cu
            )

x, info = nlp.solve(x0)

注意：定义全局变量有效但草率。必须有一种更简洁的方法来执行此操作，因为这是将数据添加到优化问题的方法。

Answer 1

将它们添加到 class 本身：

import ipopt
import numpy as np

class hs071(object):
    def __init__(self):
        pass

    def objective(self, x):
        # The callback for calculating the objective
        scale = self.scale
        
        return scale * x[0] * x[3] * np.sum(x[0:3]) + x[2]

    def gradient(self, x):
        # The callback for calculating the gradient
        scale = self.scale
        
        return np.array([
                    scale * x[0] * x[3] + scale * x[3] * np.sum(x[0:3]),
                    scale * x[0] * x[3],
                    scale * x[0] * x[3] + 1.0,
                    scale * x[0] * np.sum(x[0:3])
                    ])

    def constraints(self, x):
        # The callback for calculating the constraints
        return np.array((np.prod(x), np.dot(x, x)))

    def jacobian(self, x):
        # The callback for calculating the Jacobian
        return np.concatenate((np.prod(x) / x, 2*x))

x0 = [1.0, 5.0, 5.0, 1.0]

lb = [1.0, 1.0, 1.0, 1.0]
ub = [5.0, 5.0, 5.0, 5.0]

cl = [25.0, 40.0]
cu = [2.0e19, 40.0]

model = hs071()
model.scale = 2

nlp = ipopt.problem(
            n=len(x0),
            m=len(cl),
            problem_obj=model,
            lb=lb,
            ub=ub,
            cl=cl,
            cu=cu
            )

x, info = nlp.solve(x0)

如何在 cyipopt 中将选项传递给 objective

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