GEKKO-矩阵形式的优化 [英] GEKKO - optimization in matrix form

问题描述

我正在尝试解决一个优化问题，我需要使用2D矩阵指定问题和约束.我一直在使用SCIPY，其中需要一维阵列.我要检查GEKKO是否允许使用2D矩阵指定目标函数，边界和约束.

I am trying to solve an optimization problem where I need to specify the problem and the constraints using a 2D matrix. I have been using SCIPY, where the 1D arrays are the requirements. I want to check if GEKKO allows one to specify the objective function, bounds and constraints using a 2D matrix.

我在此处的帖子中提供了该问题的详细信息和可复制版本:

I have provided details and a reproducible version of the problem in the post here:

SCIPY-构建约束，而不必单独列出每个变量

谢谢 C

推荐答案

您可以在

You can use the m.Array function in gekko. I don't recommend that you use the np.triu() with the Gekko array because the eliminated variables will still solve but potentially be hidden from the results. Here is a solution:

import numpy as np
import scipy.optimize as opt
from gekko import GEKKO

p= np.array([4, 5, 6.65, 12]) #p = prices
pmx = np.triu(p - p[:, np.newaxis]) #pmx = price matrix, upper triangular

m = GEKKO(remote=False)
q = m.Array(m.Var,(4,4),lb=0,ub=10)
# only upper triangular can change
for i in range(4):
    for j in range(4):
        if j<=i:
            q[i,j].upper=0 # set upper bound = 0

def profit(q):
    profit = np.sum(q.flatten() * pmx.flatten())
    return profit

for i in range(4):
    m.Equation(np.sum(q[i,:])<=10)
    m.Equation(np.sum(q[:,i])<=8)
m.Maximize(profit(q))

m.solve()

print(q)

这提供了解决方案:

[[[0.0] [2.5432017412] [3.7228765674] [3.7339217013]]
 [[0.0] [0.0] [4.2771234426] [4.2660783187]]
 [[0.0] [0.0] [0.0] [0.0]]
 [[0.0] [0.0] [0.0] [0.0]]]

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