Merge pull request #168 from control-toolbox/167-bug-max-objective-with-exa

[Bug] Max objective for exa

Author: ocots

docs/problems.jl

@@ -116,7 +116,7 @@ function generate_documentation(
             nlp_oc_sol = NLPModelsIpopt.ipopt(nlp_oc; max_iter=0)
 
             # Build OptimalControl solution
-            ocp_sol = build_ocp_solution(docp, nlp_oc_sol)
+            ocp_sol = build_OCP_solution(docp, nlp_oc_sol)
 
             # get dimensions
             n = state_dimension(ocp_oc)
@@ -292,7 +292,7 @@ function generate_documentation(
         function print_numerical_comparisons(problem, docp, nlp_oc_sol, nlp_jp)
 
             # get relevant data from OptimalControl model
-            ocp_sol = build_ocp_solution(docp, nlp_oc_sol)
+            ocp_sol = build_OCP_solution(docp, nlp_oc_sol)
             t_oc = time_grid(ocp_sol)
             x_oc = state(ocp_sol).(t_oc)
             u_oc = control(ocp_sol).(t_oc)
@@ -376,7 +376,7 @@ function generate_documentation(
 
     ```@example main
     # build an ocp solution to use the plot from OptimalControl package
-    ocp_sol = build_ocp_solution(docp, nlp_oc_sol)
+    ocp_sol = build_OCP_solution(docp, nlp_oc_sol)
 
     # dimensions
     n = state_dimension(ocp_sol)

docs/src/tutorial-solve.md

@@ -49,7 +49,7 @@ nlp_sol.objective
 To recover the state, control, and costate, we recommend building an optimal control solution and using the associated getters (you can also retrieve the number of iterations and the objective value from the OCP solution):
 
 ```@example main
-ocp_sol = build_ocp_solution(docp, nlp_sol)
+ocp_sol = build_OCP_solution(docp, nlp_sol)
 
 t = time_grid(ocp_sol)  # t0, ..., tN = tf
 x = state(ocp_sol)      # function of time

src/OptimalControlProblems.jl

@@ -21,24 +21,6 @@ using OrderedCollections: OrderedDict
 using SolverCore
 import ADNLPModels: ADNLPModels, ADNLPModel
 
-# -----------------
-# SHOULD NO BE HERE ("triiiiiiiiiiiiiiit !!!" - bruit de sifflet)
-function build_ocp_solution(
-    docp::CTDirect.DOCP, nlp_solution::SolverCore.AbstractExecutionStats
-)
-    nlp_model_backend = if docp.nlp isa ADNLPModel
-        CTDirect.ADNLPBackend()
-    elseif docp.nlp isa ExaModel
-        CTDirect.ExaBackend()
-    else
-        throw(CTBase.IncorrectArgument("The NLP model is of unknown type."))
-    end
-    return CTDirect.build_OCP_solution(docp, nlp_solution; nlp_model=nlp_model_backend)
-end
-
-export build_ocp_solution
-#
-
 """
 $(TYPEDEF)
 

test/Project.toml

@@ -22,7 +22,7 @@ Ipopt = "1"
 JuMP = "1"
 MadNLPMumps = "0.5"
 NLPModels = "0.21"
-NLPModelsIpopt = "0.10"
+NLPModelsIpopt = "0.11"
 OptimalControl = "1"
 Plots = "1"
 Printf = "1"