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loops - Julia Jump 约束的嵌套迭代

问题描述

我正在尝试嵌套迭代以稍微压缩我的代码。我有一个可以运行的大型 MIP，但代码非常混乱。我想把它浓缩成向量等。

我的代码基本上如下：

using JuMP
using Gurobi
model = Model(with_optimizer(Gurobi.Optimizer))

@variable(model, x[1:11, 1:17, 1:54], Bin)

I = [(1:6),(7:11),(1:6),(7:11)]
K = [(51:54), (1:4), (1:50),(5:54)]
RHS = [4,4,0,0]

@constraints(model, begin
 [i in I[1]], sum(x[i,17,k] for k in K[1]) == RHS[1]
 [i in I[2]], sum(x[i,17,k] for k in K[2]) == RHS[2]
 [i in I[3]], sum(x[i,17,k] for k in K[3]) == RHS[4]
 [i in I[4]], sum(x[i,17,k] for k in K[4]) == RHS[4]
end
)

本质上，我想将所有这些约束浓缩到一行，就像在程序中进一步完成的那样，我有类似的约束，有 54 次迭代。

我试过了：

@constraint(model, 
    for (a,b,c) in zip(I, K, RHS)
        [i in a], sum(x[i,17,k] for k in b) == c
    end
)

和其他一些组合，比如

@constraint(model, [(a,b,c) in zip(I, K, RHS), i in a], sum(x[i,17,k] for k in b) == c)

但它对我来说并不合适——我会遇到加载错误或重复迭代器错误。

帮助将不胜感激！！！:-)

标签： loopsfor-loopjuliamixed-integer-programmingjulia-jump

解决方案

这个版本对我有用：

@constraint(
    model, 
    [(a, b, c) in zip(I, K, RHS), i in a], 
    sum(x[i, 17, k] for k in b) == c
)

另一个更易读的版本是

for (a, b, c) in zip(I, K, RHS)
   @constraint(model, [i in a], sum(x[i, 17, k] for k in b) == c)
end

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