Iterating SparseAxisArray does not preserve order about jump.jl HOT 4 CLOSED

julia> using JuMP

julia> A = [[1, 2, 10], [2, 3, 30]]
2-element Vector{Vector{Int64}}:
 [1, 2, 10]
 [2, 3, 30]

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

julia> @variable(model, x[i in 1:2, j in A[i]])
JuMP.Containers.SparseAxisArray{VariableRef, 2, Tuple{Int64, Int64}} with 6 entries:
  [1, 1 ]  =  x[1,1]
  [1, 10]  =  x[1,10]
  [1, 2 ]  =  x[1,2]
  [2, 2 ]  =  x[2,2]
  [2, 3 ]  =  x[2,3]
  [2, 30]  =  x[2,30]

julia> @constraint(model, x[1, :] in SecondOrderCone())
[x[1,2], x[1,10], x[1,1]] ∈ MathOptInterface.SecondOrderCone(3)

julia> x[1, :] isa AbstractVector{<:AbstractJuMPScalar}
true

julia> y = x[1, :]
JuMP.Containers.SparseAxisArray{VariableRef, 1, Tuple{Int64}} with 3 entries:
  [1 ]  =  x[1,1]
  [10]  =  x[1,10]
  [2 ]  =  x[1,2]

julia> [y[k] for k in eachindex(y)]
3-element Vector{VariableRef}:
 x[1,2]
 x[1,10]
 x[1,1]

julia> @constraint(model, x[1, :] >= 0)
[x[1,2], x[1,10], x[1,1]] ∈ MathOptInterface.Nonnegatives(3)

julia> @constraint(model, x[1, :] in SecondOrderCone())
[x[1,2], x[1,10], x[1,1]] ∈ MathOptInterface.SecondOrderCone(3)

julia> using JuMP

julia> A = [[1, 2, 10], [2, 3, 30]]
2-element Vector{Vector{Int64}}:
 [1, 2, 10]
 [2, 3, 30]

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

julia> @variable(model, x[i in 1:2, j in A[i]])
JuMP.Containers.SparseAxisArray{VariableRef, 2, Tuple{Int64, Int64}} with 6 entries:
  [1, 1 ]  =  x[1,1]
  [1, 10]  =  x[1,10]
  [1, 2 ]  =  x[1,2]
  [2, 2 ]  =  x[2,2]
  [2, 3 ]  =  x[2,3]
  [2, 30]  =  x[2,30]

julia> y = x[1, :]
JuMP.Containers.SparseAxisArray{VariableRef, 1, Tuple{Int64}} with 3 entries:
  [1 ]  =  x[1,1]
  [10]  =  x[1,10]
  [2 ]  =  x[1,2]

julia> z = rand(3)
3-element Vector{Float64}:
 0.508529766285344
 0.32511007041899975
 0.6771958164053773

julia> using LinearAlgebra

julia> dot(z, y)
0.508529766285344 x[1,2] + 0.32511007041899975 x[1,10] + 0.6771958164053773 x[1,1]

julia> model = Model();

julia> @variable(model, x[i in 1:2], container = SparseAxisArray)
JuMP.Containers.SparseAxisArray{VariableRef, 1, Tuple{Int64}} with 2 entries:
  [1]  =  x[1]
  [2]  =  x[2]

julia> dot(1:2, x)
x[2] + 2 x[1]