如何在 Gekko 中动态构建约束? [英] How could constraints be dynamically constructed in gekko?

问题描述

我是gekko的新手，想在我的线性编程问题中使用它.

I'm a newbie in gekko, and want to use it in my linear programming problems.

我在单独的词典(my_vars，Cost，Min和Max)中以变量名作为键，具有变量名，成本，最小和最大界限，目的是通过确定满足约束条件的变量数量来最大程度地降低总成本.

I have variable names, costs, minimum and maximum bounds in separate dictionaries (my_vars, Cost, Min and Max) with variable names as their keys, and the objective is minimizing total cost with determining the amount of variables satisfying the constraints.

我做了如下；

LP = GEKKO(remote=False)
vars = LP.Array(LP.Var, (len(my_vars)))
i=0
for xi in vars:
    xi.lower = Min[list(my_vars)[i]]
    xi.upper = Max[list(my_vars)[i]]
    i += 1

在这里，我想使用可变的原始名称代替xi，有什么办法吗?

Here I'd like to use variable original names instead of xi, is there any way?

它继续为；

LP.Minimize(sum(float(Cost[list(my_vars)[i]])*vars[i] for i in range(len(my_vars))))
LP.Equation(sum(vars) == 100)

我在两个熊猫数据帧文件中都有约束的左侧(LHS)(变量系数)和右侧(RHS)编号，并且喜欢使用for循环构造方程.

Also I have constraint's left hand side (LHS) (coefficients of variables) and right hand side (RHS) numbers in two pandas data frame files, and like to construct equations using a for loop.

我不知道该怎么做?

推荐答案

以下是使用字典值构造问题的一种方法:

Here is one way to use your dictionary values to construct the problem:

from gekko import GEKKO

# stored as list
my_vars = ['x1','x2']
# stored as dictionaries
Cost = {'x1':100,'x2':125}
Min = {'x1':0,'x2':0}
Max = {'x1':70,'x2':40}

LP = GEKKO(remote=False)
va = LP.Array(LP.Var, (len(my_vars)))  # array
vd = {}                                # dictionary
for i,xi in enumerate(my_vars):
    vd[xi] = va[i]
    vd[xi].lower = Min[xi]
    vd[xi].upper = Max[xi]

# Cost function
LP.Minimize(LP.sum([Cost[xi]*vd[xi] for xi in my_vars])) 
# Summation as an array
LP.Equation(LP.sum(va)==100)
# This also works as a dictionary
LP.Equation(LP.sum([vd[xi] for xi in my_vars])==100)
LP.solve(disp=True)

for xi in my_vars:
    print(xi,vd[xi].value[0])
print ('Cost: ' + str(LP.options.OBJFCNVAL))

这将产生一个解决方案:

This produces a solution:

EXIT: Optimal Solution Found.

 The solution was found.

 The final value of the objective function is  10750.00174236579
 
 ---------------------------------------------------
 Solver         :  IPOPT (v3.12)
 Solution time  :  0.012199999999999996 sec
 Objective      :  10750.00174236579
 Successful solution
 ---------------------------------------------------
 

x1 69.999932174
x2 30.0000682
Cost: 10750.001742

以下是一些利用Gekko进行高效线性编程的示例.问题稀疏.

Here are a few examples of efficient linear programming with Gekko by exploiting problem sparsity.

这篇关于如何在 Gekko 中动态构建约束?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！