问题
I would like to ask you regarding on the Linear Program for optimization.
I have an objective function, and constraint functions as below,
- variables(x1, x2, x3, x4, x5, x6) are quantities of the products, and the quantities of products have to be fixed numbers now.
the goal of this problem is the optimizing the quantities of products.
Objective Function (c.T * [x1, x2, x3, x4, x5, x6])
[[c11, c12, c13, c14, c15 c16], [c21, c22, c23, c24, c25, c26], X [x1, x2, x3, x4, x5, x6] [c31, c32, c33, c34, c35, c36], [c41, c42, c43, c44, c45, c45]]
The result that I would like to optimize is going to be as below
c11*x1 + c12*x2 + c13*x3 + c14*x4 + c15*x5 + c16*x6 + c21*x1 + c22*x2 + c23*x3 + c24*x4 + c25*x5 + c26*x6 + c31*x1 + c32*x2 + c33*x3 + c34*x4 + c35*x5 + c36*x6 + c41*x1 + c42*x2 + c43*x3 + c44*x4 + c45*x5 + c46*x6 = optimized value- Constraint Function
1) constraint_1
5500000*x1+2500000*x2+825000*x3+5500000*x4+5500000*x5+5500000*x6 <= 800000000
2) constraint_2
x1 <= 10 x2 <= 10 x3 <= 10 x4 <= 10 x5 <= 10 x6 <= 10
The problem that I am suffering from is the in the "Objective Function of Cs(c1,1 ~ c4,5)".
I have solved the Linear Programming that has integers values in the Objective Functions, but not the matrix.
I have tried all other ways that I could, but now I really need helps on this.
Please kindly suggest me any kind of ideas or codes for this question.
回答1:
Suppose you have store the original cij in a numpy array, you might like to sum up terms like c11+c21+c31+c41 first. This can be done by summing up each column, try c.sum(axis = 0)
>>> import numpy as np
>>> c = np.arange(24).reshape(4,6)
>>> c
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23]])
>>> c = c.sum(axis=0)
>>> c
array([36, 40, 44, 48, 52, 56])
来源：https://stackoverflow.com/questions/53649989/linear-programming-with-cvxpy