与矩阵一起使用的Python Pulp [英] Python Pulp using with Matrices

问题描述

经过数年的Matlab，我对Python还是很陌生.我正在尝试使用Pulp设置整数线性程序.

I am still very new to Python, after years and years of Matlab. I am trying to use Pulp to set up an integer linear program.

给出一个数字数组:

{P[i]:i=1...N}

我想最大化:

sum( x_i P_i )

受约束

A x <= b
A_eq x = b_eq

并带有边界(基于矢量的边界)

and with bounds (vector based bounds)

LB <= x <= UB

但是，在纸浆中，我看不到如何正确进行向量声明.我正在使用:

In pulp however, I don't see how to do vector declarations properly. I was using:

RANGE = range(numpy.size(P))
x = pulp.LpVariable.dicts("x", LB_ind, UB_ind, "Integer")

，我只能输入单个范围(因此只能输入1个数字).

where I can only enter individual bounds (so only 1 number).

prob = pulp.LpProblem("Test", pulp.LpMaximize)
prob += pulp.lpSum([Prices[i]*Dispatch[i] for i in RANGE])

并且出于约束，我真的必须逐行执行此行吗?看来我缺少了一些东西.我将不胜感激.该文档讨论了一个简短的示例.就我而言，变量的数量是几千.

and for the constraints, do I really have to do this line per line? It seems that I am missing something. I would appreciate some help. The documentation discusses a short example. The number of variables in my case is a few thousand.

推荐答案

您可以在变量初始化后设置 lowBound 和 upBound.您可以使用

You can set the lowBound and upBound on variables after the initialization. You can create an array of variables with

LB[i] <= x[i] <= UB[i]

使用以下代码.

x = pulp.LpVariable.dicts("x", RANGE,  cat="Integer")
for i in x.viewkeys():
     x[i].lowBound = LB_ind[i]
     x[i].upBound = UB_ind[i]

LpVariable.dict的第二个参数是决策变量的索引集，而不是它们的下限.

The second parameter to LpVariable.dict is the index set of the decision variables, not their lower bounds.

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