Unable to delete constraints stored in dictionaries using JuMP@.constraint macro

I am unable to delete the constraints which is created and stored in a dictionary using the JuMP.@constraint macro.
the constraints are added in the form
for i in array
d[i] = JuMP.@constraint(model, variable[i] == constant)
end
when i delete them using
for i in array
delete(model, d[i])
end
it doesnt seem to work. any Idea where i am going wrong or what can be done to fix it?

This is a known issue. Please read: https://github.com/jump-dev/JuMP.jl/issues/1956

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So from what i gather there is no way to delete it… only modification is the option?
Or do i have to reformulate the whole problem each time before I solve it? I have an equality constraint on some variables which i want to remove , add a few different constraints and re run the optimization

Ah, I mis-read your question. This should work:

julia> model = Model()
A JuMP Model
Feasibility problem with:
Variables: 0
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.

julia> array = [1, 2, 3]
3-element Array{Int64,1}:
 1
 2
 3

julia> constant = 1
1

julia> @variable(model, x[array])
1-dimensional DenseAxisArray{VariableRef,1,...} with index sets:
    Dimension 1, [1, 2, 3]
And data, a 3-element Array{VariableRef,1}:
 x[1]
 x[2]
 x[3]

julia> d = Dict()
Dict{Any,Any} with 0 entries

julia> for i in array
           d[i] = @constraint(model, x[i] == constant)
       end

julia> println(model)
Feasibility
Subject to
 x[1] = 1.0
 x[2] = 1.0
 x[3] = 1.0


julia> for i in array
           delete(model, d[i])
       end

julia> println(model)
Feasibility
Subject to

Thats useful…Can you solve the given model-
julia> println(model)
Feasibility
Subject to
x[1] = 1.0
x[2] = 1.0
x[3] = 1.0
write its solution and then delete the constraints? I for some reason am still unable to do so
Also i was using JuMP.@constraint is it different than using @constraint?

Please show a minimal version of the code you are trying to run the demonstrates the error.

The first post of PSA: make it easier to help you has some good tips to read first.

1 Like

Ok.iwas actually thinking perhaps I should use a call back routine… given I an solving the problem then using the solution to add delete constraints and solving again iteratively… will post code shortly