Enabled Using Repartitioned Graphs, Added Display

lib/PlasmoBenders/Project.toml

@@ -1,7 +1,7 @@
 name = "PlasmoBenders"
 uuid = "491f1417-53b2-48aa-b9da-44cdd6c031b7"
 authors = ["David Cole"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 Plasmo = "d3f7391f-f14a-50cc-bbe4-76a32d1bad3c"

lib/PlasmoBenders/README.md

@@ -18,12 +18,12 @@ Pkg.add("PlasmoBenders")
 
 [Benders decomposition](https://en.wikipedia.org/wiki/Benders_decomposition) (BD) is a decomposition approach that breaks problems into a master problem and a subproblem(s) and is typically applied to linear and mixed integer linear programs. BD is an iterative algorithm that can be useful for problems where there are a set of complicating variables (in the master problem) that, once fixed, make the subproblem easier to solve. An iteration of BD generally includes 1) solving the master problem, 2) passing the solution of the master problem to the subproblem, 3) solving the subproblem with the master problem solution, and 4) passing primal and dual information from the subproblem to the master problem and forming cutting planes on the master problem. PlasmoBenders applies this approach to graph-based problems where each subgraph of an OptiGraph is the master problem or a subproblem. PD is applied to the graph based on the user-defined subproblems. 
 
-Nested Benders Decomposition (NBD; also called [dual dynamic programming](https://www-sciencedirect-com.ezproxy.library.wisc.edu/science/article/pii/S0098135421000430)) uses similar ideas to BD but can have a sequence of subproblems (i.e., not all subproblems are connected to the original master problem, forming a nested structure). NBD can be applied to graphs with a tree structure, and NBD is likewise implemented in PlasmoBenders.
+Nested Benders Decomposition (NBD; also called [dual dynamic programming](https://doi.org/10.1016/j.compchemeng.2021.107265)) uses similar ideas to BD but can have a sequence of subproblems (i.e., not all subproblems are connected to the original master problem, forming a nested structure). NBD can be applied to graphs with a tree structure, and NBD is likewise implemented in PlasmoBenders.
 
 > [!NOTE]  
 > PlasmoBenders requires Plasmo v0.6.2 or later
 
-PlasmoBenders is built on a `BendersOptimizer` object which requires a user-defined graph and a subgraph of that graph as the root (master problem) graph. After the information is past, the `BendersOptimizer` constructor updates the graph to apply BD or NBD. `JuMP.optimize!` is extended so that the iterative BD/NBD algorithm is applied to find a solution. 
+PlasmoBenders is built on a `BendersAlgorithm` object which requires a user-defined graph and a subgraph of that graph as the root (master problem) graph. After the information is passed, the `BendersAlgorithm` object is instantiated and can be solved with the `run_algorithm!` function.
 
 
 ### Simple Exmaple
@@ -63,16 +63,16 @@ set_to_node_objectives(g22)
 
 solver = optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false)
 
-BendersOpt = BendersOptimizer(g0, g1; solver = solver)
+BendersAlg = BendersAlgorithm(g0, g1; solver = solver)
 
-optimize!(BendersOpt)
+run_algorithm!(BendersAlg)
 ```
 
-The `BendersOptimizer` constructor takes the overall graph, `g0` as the first object and then the root/master subgraph `g1` as the second argument. 
+The `BendersAlgorithm` constructor takes the overall graph, `g0` as the first object and then the root/master subgraph `g1` as the second argument. 
 
 ### Additional Functionality
 
-PlasmoBenders includes additional functionality. The following keyword arguments can be passed to the `BendersOptimizer` constructor. These include the following key word arguments: 
+PlasmoBenders includes additional functionality. The following keyword arguments can be passed to the `BendersAlgorithm` constructor. These include the following key word arguments: 
 
  * `max_iters` - maximum number of iterations to use
  * `tol` - termination tolerance between upper and lower bounds

lib/PlasmoBenders/src/initialize.jl

@@ -140,8 +140,12 @@ function _add_complicating_variables!(
             con_obj = constraint_object(link)
             vars = con_obj.func.terms.keys
 
-            next_object_link_vars = [var for var in vars if source_graph(JuMP.owner_model(var)) == next_object]
-            last_object_link_vars = [var for var in vars if source_graph(JuMP.owner_model(var)) == last_object]
+            # Get the variables from the constraint in the next_object
+            next_object_nodes = all_nodes(next_object)
+            last_object_nodes = all_nodes(last_object)
+
+            next_object_link_vars = [var for var in vars if JuMP.owner_model(var) in next_object_nodes]
+            last_object_link_vars = [var for var in vars if JuMP.owner_model(var) in last_object_nodes]
 
             # Get the optinodes containing the next set of variables
             #next_optinode = optinode(next_object_vars[1]) #NEXT: Fix this!
@@ -239,8 +243,11 @@ function _add_complicating_variables!(
             vars = con_obj.func.terms.keys
 
             # Get the variables from the constraint in the next_object
-            next_object_link_vars = [var for var in vars if source_graph(JuMP.owner_model(var)) == next_object]
-            last_object_link_vars = [var for var in vars if source_graph(JuMP.owner_model(var)) == last_object]
+            next_object_nodes = all_nodes(next_object)
+            last_object_nodes = all_nodes(last_object)
+
+            next_object_link_vars = [var for var in vars if JuMP.owner_model(var) in next_object_nodes]
+            last_object_link_vars = [var for var in vars if JuMP.owner_model(var) in last_object_nodes]
             next_object_copy_vars = [var_copy_map[var] for var in last_object_link_vars]
 
             # Get the set of nodes in the next_object that are included in the constraint

lib/PlasmoBenders/src/utils.jl

@@ -1,3 +1,28 @@
+# Enable printing the optimizer
+function Base.string(optimizer::BendersAlgorithm)
+    return return @sprintf(
+        """
+        A BendersAlgorithm
+        -------------------------------------------
+        %32s %9s
+        %32s %9s
+        %32s %9s
+        %32s %9s
+        """,
+        "Num subproblem subgraphs:",
+        length(optimizer.solve_order),
+        "MIP subproblems (nonroot):",
+        optimizer.is_MIP,
+        "Absolute Tolerance:",
+        optimizer.tol,
+        "Maximum Iterations:",
+        optimizer.max_iters,
+    )
+end
+
+Base.print(io::IO, optimizer::BendersAlgorithm) = Base.print(io, Base.string(optimizer))
+Base.show(io::IO, optimizer::BendersAlgorithm) = Base.print(io, optimizer)
+
 function _get_hyper_projection(optimizer::BendersAlgorithm, graph::Plasmo.OptiGraph)
     if haskey(optimizer.ext, "_projection")
         return optimizer.ext["_projection"]
@@ -413,7 +438,7 @@ function get_upper_bounds(optimizer::BendersAlgorithm; monotonic = true)
         if optimizer.current_iter == 0
             return ubs
         else
-            return [minimum(ubs[1:i] for i in 1:length(ubs))]
+            return [minimum(ubs[1:i]) for i in 1:length(ubs)]
         end
     else
         return ubs