control-toolbox · ocots · Aug 28, 2025 · Aug 28, 2025
diff --git a/Project.toml b/Project.toml
@@ -4,12 +4,14 @@ authors = ["Olivier Cots <olivier.cots@toulouse-inp.fr>"]
 version = "0.2.1"
 
 [deps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 CTBase = "54762871-cc72-4466-b8e8-f6c8b58076cd"
 CTDirect = "790bbbee-bee9-49ee-8912-a9de031322d5"
 CTModels = "34c4fa32-2049-4079-8329-de33c2a22e2d"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
+SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [weakdeps]
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -20,6 +22,7 @@ JuMPModels = "JuMP"
 OptimalControlModels = "OptimalControl"
 
 [compat]
+ADNLPModels = "0.8"
 CTBase = "0.16"
 CTDirect = "0.16"
 CTModels = "0.6"
@@ -28,4 +31,5 @@ ExaModels = "0.9"
 JuMP = "1"
 OptimalControl = "1"
 OrderedCollections = "1"
+SolverCore = "0.3"
 julia = "1.10"
diff --git a/docs/problems.jl b/docs/problems.jl
@@ -76,7 +76,7 @@ function generate_documentation(
     )
 
     # build an optimal control solution
-    ocp_sol = build_OCP_solution(docp; primal=nlp_sol.solution, dual=nlp_sol.multipliers, docp_solution=nlp_sol)
+    ocp_sol = build_ocp_solution(docp, nlp_sol)
     nothing # hide
     ```
 
@@ -246,7 +246,7 @@ function generate_documentation(
         )
 
         # build an optimal control solution
-        ocp_sol = build_OCP_solution(docp; primal=nlp_sol.solution, dual=nlp_sol.multipliers, docp_solution=nlp_sol)
+        ocp_sol = build_ocp_solution(docp, nlp_sol)
 
         # plot the OptimalControl solution
         plt = plot(

diff --git a/docs/src/assets/Manifest.toml b/docs/src/assets/Manifest.toml
@@ -1042,7 +1042,7 @@ uuid = "5f98b655-cc9a-415a-b60e-744165666948"
 version = "1.1.1"
 
 [[deps.OptimalControlProblems]]
-deps = ["CTBase", "CTDirect", "CTModels", "DocStringExtensions", "ExaModels", "OrderedCollections"]
+deps = ["ADNLPModels", "CTBase", "CTDirect", "CTModels", "DocStringExtensions", "ExaModels", "OrderedCollections", "SolverCore"]
 path = "/Users/ocots/Research/logiciels/dev/control-toolbox/OptimalControlProblems"
 uuid = "59046045-fb9c-4c23-964f-ff0a25704f96"
 version = "0.2.1"

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -21,7 +21,7 @@ Pkg.add("OptimalControlProblems")
 - [0Yassine0](https://github.com/0Yassine0)
 - [frapac](https://github.com/frapac)
 - [BaptisteCbl](https://github.com/BaptisteCbl)
-- [COPS: Large-Scale Optimization Problems](https://www.mcs.anl.gov/~more/cops) and [COPSBenchmark.jl](github.com/MadNLP/COPSBenchmark.jl)
+- [COPS: Large-Scale Optimization Problems](https://www.mcs.anl.gov/~more/cops) and [COPSBenchmark.jl](https://github.com/MadNLP/COPSBenchmark.jl)
 - [BOCOP - A collection of examples](https://project.inria.fr/bocop/files/2017/05/Examples-BOCOP.pdf)
 
 ## Reproducibility

diff --git a/docs/src/tutorial-solve.md b/docs/src/tutorial-solve.md
@@ -44,12 +44,7 @@ sol.iter
 To recover the state, control, and costate, we recommend building an optimal control solution and using the associated getters:
 
 ```@example main
-ocp_sol = build_OCP_solution(
-    docp;
-    primal=sol.solution,
-    dual=sol.multipliers,
-    docp_solution=sol,
-)
+ocp_sol = build_ocp_solution(docp, sol)
 
 t = time_grid(ocp_sol)  # t0, ..., tN = tf
 x = state(ocp_sol)      # function of time

diff --git a/src/OptimalControlProblems.jl b/src/OptimalControlProblems.jl
@@ -3,16 +3,36 @@ module OptimalControlProblems
 using CTBase
 using CTDirect
 import CTModels: CTModels, time_grid, state, control, costate
-import ExaModels: ExaModels, variable
+import ExaModels: ExaModels, ExaModel, variable
 using DocStringExtensions
 using OrderedCollections: OrderedDict
+using SolverCore
+import ADNLPModels: ADNLPModels, ADNLPModel
 
 # -----------------
 # SHOULD NO BE HERE
 nlp_model(docp::CTDirect.DOCP) = docp.nlp
 ocp_model(docp::CTDirect.DOCP) = docp.ocp
+function build_ocp_solution(docp::CTDirect.DOCP, nlp_solution::SolverCore.AbstractExecutionStats)
+    nlp_model_backend = if nlp_model(docp) isa ADNLPModel 
+        CTDirect.ADNLPBackend()
+    elseif nlp_model(docp) isa ExaModel 
+        CTDirect.ExaBackend()
+    else
+        throw(CTBase.IncorrectArgument("The NLP model is of unknown type."))
+    end
+    return CTDirect.build_OCP_solution(
+        docp; 
+        primal=nlp_solution.solution, 
+        dual=nlp_solution.multipliers, 
+        mult_LB=nlp_solution.multipliers_L,
+        mult_UB=nlp_solution.multipliers_U,
+        nlp_model=nlp_model_backend,
+        docp_solution=nlp_solution
+    )
+end
 
-export nlp_model, ocp_model
+export nlp_model, ocp_model, build_ocp_solution
 
 # -----------------
 

diff --git a/test/figures/init/beam.pdf b/test/figures/init/beam.pdf
diff --git a/test/figures/solution/beam.pdf b/test/figures/solution/beam.pdf
diff --git a/test/runtests.jl b/test/runtests.jl
@@ -34,9 +34,9 @@ end
 # ]
 # list_of_problems = setdiff(list_of_problems, problems_to_exclude)
 
-# list_of_problems = [
-#     :beam
-# ]
+list_of_problems = [
+#    :beam
+]
 
 # The list of all the problems to test
 const LIST_OF_PROBLEMS = deepcopy(list_of_problems)

diff --git a/test/test_quick.jl b/test/test_quick.jl
@@ -29,12 +29,7 @@ function test_quick()
             docp = OptimalControlProblems.eval(f)(OptimalControlBackend(); N=N)
             nlp = nlp_model(docp)
             nlp_sol = NLPModelsIpopt.ipopt(nlp; kwargs...)
-            sol = build_OCP_solution(
-                docp;
-                primal=nlp_sol.solution,
-                dual=nlp_sol.multipliers,
-                docp_solution=nlp_sol,
-            )
+            sol = build_ocp_solution(docp, nlp_sol)
             o_oc = objective(sol)
 
             ############### JuMP ###############

diff --git a/test/utils.jl b/test/utils.jl
@@ -128,12 +128,7 @@ function comparison(; max_iter, test_name)
             nlp_sol = NLPModelsIpopt.ipopt(nlp; Options...)
 
             # build the solution
-            sol = build_OCP_solution(
-                docp;
-                primal=nlp_sol.solution,
-                dual=nlp_sol.multipliers,
-                docp_solution=nlp_sol,
-            )
+            sol = build_ocp_solution(docp, nlp_sol)
 
             sol_oc = deepcopy(sol) # for plotting