Question

我对Python还是很陌生，并试图找到与Excel Solver函数等效的Python。假设我有以下输入：

import math

totalvolflow=150585.6894
gaspercentvol=0.1
prodmod=1224
blpower=562.57
powercons=6

gasvolflow=totalvolflow*gaspercentvol
quantity=math.ceil(gasvolflow/prodmod)
maxpercentvol=powercons*totalvolflow*prodmod/blpower

我想通过更改gaspercentvol找到maxpercentvol的最大值具有以下约束：

quantity*powercon<blpower

任何帮助将不胜感激。

Answer 1

根据：

maxpercentvol=powercons*totalvolflow*prodmod/blpower

maxpercentvol为1,965,802.12765274，无论gaspercentvol的值如何。

Answer 2

我不是一个精明的Excel用户，但是我的理解是Excel Solver是线性/非线性编程求解器，对吧？

在这种情况下，Python可以使用良好的优化库（例如SciPy's optimize）很好地服务。为了解决您的特定示例，您可以使用诸如：

import numpy as np
from scipy.optimize import minimize
import math

"""
x[0] = powercons
x[1] = totalvolflow
x[2] = prodmod
x[3] = blpower
x[4] = gaspercentvol
"""

def objective(x):
    """
    -powercons*totalvolflow*prodmod/blpower
    To maximize, we multiply our objective by -1
    """
    return -1.0*(x[0]*x[1]*x[2]/x[3])


def constraint1(x):
    """
    0 < blpower - quantity*powercon
    minimize expects constrains to be greater or equal than zero
    """
    return x[3] - (math.ceil(x[1]*x[4]/x[2])*x[0])

# Initial guess
n = 5
first_guess = np.zeros(n)
first_guess[0] = 6.0
first_guess[1] = 150585.6894
first_guess[2] = 1224
first_guess[3] = 562.57
first_guess[4] = 0.1


# constrains
constraint1_dict = {'type': 'ineq', 'fun': constraint1} 

# bounds
problem_bounds = (
    (6, 6),
    (150585.6894, 150585.6894),
    (1224, 1224),
    (562.57, 562.57),
    (0.05, 25)
)

# optimize
solution = minimize(
    objective,
    first_guess,
    bounds=problem_bounds,
    constraints=[constraint1_dict]
)

solution_values = solution.x

print(solution_values)

等同于Excel Solver的Python：获取已知约束的最大值

2 个答案: