将 scipy linprog 中的设置与 PuLP 匹配

如何解决将 scipy linprog 中的设置与 PuLP 匹配

我在 SciPy linprog 中设置了一个相当简单的 LP 模型，但在 PuLP 中设置时遇到问题。

我打算增加 linprog 无法处理的更多复杂性，所以想改为设置它。虽然看起来不像 linprog 那样用户友好！

linprog 设置如下：

import pandas as pd
df = pd.read_excel('df.xlsx')

from scipy.optimize import linprog

#1d n-element array of ones,*-1 to minimize the sum of the variables to solve for: x1,x2 ... xn
constants=np.array(df['Ones'])*(-1)

# equations for the inequality constraints,simply a M x n array
A_ub=df.loc[:,'col_a':'col_n'].to_numpy().transpose()

# array of M zeros as bounds for above inequality constraint
b_ub = np.array((A_ub.shape[0])*[0])

# lower and upper bounds set for the elements
bounds=[(0,x) for x in list(df['bounds_max'])]

# the set up of the equation
main_result=linprog(constants,A_ub=A_ub,b_ub=b_ub,A_eq=None,b_eq=None,bounds=bounds,method='interior-point',callback=None,options=None,x0=None)

以上一切正常，但不确定如何在 PuLP 中设置相同的内容？我到目前为止的尝试：

# importing the package
import pulp as pl

# defining the problem
prob = pl.LpProblem("df",pl.LpMinimize)

# defining the set of variables to solve for like above (x1,x2,... xn) with the same constraints like in 'bounds' above. different 'x's have different upper bounds
x = pl.LpVariable.matrix('x',list(range(len(df['Helper']))),[i for i in list(df['bounds_max'])])

# the equivalent to 'constant' above - minimize the sum of 'x1,... xn'
prob += pl.lpSum(x for j in range(len(list(df['Ones']))))

# the same equations for the inequality constraints
A_ub=df.loc[:,'col_a':'col_a'].to_numpy().transpose()

# here is where it breaks down,the multiplying each 'x' element be each element in each row of the array
prob += pl.lpSum(x*[a for a in A_ub]) <= 0

# Solve the problem
prob.solve()

从倒数第二行收到错误消息“类型错误：不能将序列乘以非整数的‘列表’类型”。此外，上限的列表推导式不像 linprog 那样工作。（类型错误：必须是实数，而不是生成器）

有人知道正确的设置吗？