Define a variable that depends on a previously defined variable in JuMP

New to Julia
1

Hello all,

I am trying to define a variable that is dependent on a previously defined variable. A close example is as follows:

N=10
nodes = 1:N
model=Model()
@variable(model,is_not_damaged_nodes[i in nodes], Bin)
@variable(model,ω[ i in nodes[is_not_damaged_nodes.==0] ],Bin)

The error message is:

**ERROR: MethodError: no method matching iterate(::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}})**
Closest candidates are:
  iterate(::Core.SimpleVector) at essentials.jl:589
  iterate(::Core.SimpleVector, ::Any) at essentials.jl:589
  iterate(::ExponentialBackOff) at error.jl:171
  ...
Stacktrace:
 [1] copyto!(::Array{Variable,1}, ::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}}) at .\abstractarray.jl:646
 [2] _collect(::UnitRange{Int64}, ::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}}, ::Base.HasEltype, ::Base.HasLength) at .\array.jl:563
 [3] collect(::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}}) at .\array.jl:557
 [4] broadcastable(::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}}) at .\broadcast.jl:617
 [5] broadcasted(::Function, ::JuMP.JuMPArray{Variable,1,Tuple{UnitRange{Int64}}}, ::Int64) at .\broadcast.jl:1171
 [6] top-level scope at C:\Users\yuj5\.juliapro\JuliaPro_v1.0.5-1\packages\JuMP\I7whV\src\macros.jl:231

Could you give some suggestions to fix the bug? Thanks a lot!

JZ

1 Like
2

You can’t define variables that depend on other variables like this.

What is the outcome you are trying to achieve? There’s probably a better way to do it.

1 Like
3

Thank you for pointing out the error!

I am trying to formulate a network recovery problem. A part of this problem is to define a variable ω indicating whether or not damaged nodes in a network will be selected to be restored. Therefore, the number of ω is dependent on the number of damaged nodes, which is a random variable as well due to the uncertainty around the disruption.

4

One option depending on the rest of your model if you are willing to solve an integer program is to use a disjunctive constraint. Define Omega for all nodes and then add two constraints. The following pseudo code illustrates the idea:

Omega[i] <= M*(1-is_damaged_node[i])
Omega[i] >= -M*(1-is_damaged_node[i])

is_damaged_node[i] is binary for all I

And the variable M is chosen to be big enough that when is_damaged_node[i] == 0 the variable Omega[i]is unconstrained and is otherwise set to zero.

Reference: despite the silly illustrations, the following presentation does work through several examples Tutorials | Optimization Methods in Management Science | Sloan School of Management | MIT OpenCourseWare

5

As Tom in the funny but wonderful illustration said, “It is a little tricky, but I like it.”

Thanks.