`dual(subgraph, link_ref)` does not work like `value(subgraph, var_ref)`

[dlcole3]

When using expanded subgraphs with the same variables defined on multiple subgraphs, the value of the variables in the different subgraphs after optimization can be queried by calling value(subgraph, var_ref). However, the same is not true for the dual function when the same linking constraints are defined on different subgraphs.

The code below replicates this issue. In the last print statement, I would expect that calling dual(expanded_subgraphs[1], middle_link) would return the value of dual1, but it returns dual2.

using Plasmo, JuMP, Ipopt

graph = OptiGraph()
set_optimizer(graph, Ipopt.Optimizer)

@optinode(graph, nodes[1:4])
for (i, node) in enumerate(nodes)
    @variable(node, x >= i)
    @objective(node, Min, 2 * x)
end
for i in 1:3
    @linkconstraint(graph, nodes[i + 1][:x] + nodes[i][:x] >= i * 4)
end

node_membership = [1, 1, 2, 2]
hypergraph, hyper_map = gethypergraph(graph)
partition = Partition(hypergraph, node_membership, hyper_map)
apply_partition!(graph, partition)
subgraphs = getsubgraphs(graph)
expanded_subgraphs = Plasmo.expand.(graph, subgraphs, 1)
set_optimizer(expanded_subgraphs[1], Ipopt.Optimizer)
set_optimizer(expanded_subgraphs[2], Ipopt.Optimizer)

middle_link = graph.optiedges[1].linkrefs[1]

optimize!(expanded_subgraphs[1])
dual1 = dual(middle_link)

optimize!(expanded_subgraphs[2])
dual2 = dual(middle_link)

println(dual(expanded_subgraphs[1], middle_link) == dual1) # this line is false

I believe this is because the code here depends on the last_solution_id attribute of the optiedge, which links to the solution from expanded_subgraphs[2]. For now, I am able to get around this by writing a wrapper to set the last_solution_id of the optiedge to point to expanded_subgraphs[1] as in the code below.

graph.optiedges[1].backend.last_solution_id = expanded_subgraphs[1].id

[jalving]

This should now be fixed in main