control-toolbox · jbcaillau · Sep 24, 2025 · Sep 20, 2025 · Sep 20, 2025 · Sep 20, 2025
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "OptimalControl"
 uuid = "5f98b655-cc9a-415a-b60e-744165666948"
 authors = ["Olivier Cots <olivier.cots@toulouse-inp.fr>"]
-version = "1.1.1"
+version = "1.1.2"
 
 [deps]
 ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
@@ -17,10 +17,10 @@ ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
 [compat]
 ADNLPModels = "0.8"
 CTBase = "0.16"
-CTDirect = "0.16"
+CTDirect = "0.17"
 CTFlows = "0.8"
 CTModels = "0.6"
-CTParser = "0.6"
+CTParser = "0.7"
 CommonSolve = "0.2"
 DocStringExtensions = "0.9"
 ExaModels = "0.9"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -18,6 +18,7 @@ JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
+MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
@@ -28,7 +29,7 @@ Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 [compat]
 ADNLPModels = "0.8"
 CTBase = "0.16"
-CTDirect = "0.16"
+CTDirect = "0.17"
 CTFlows = "0.8"
 CTModels = "0.6"
 CTParser = "0.6"
@@ -45,6 +46,7 @@ JSON3 = "1"
 LinearAlgebra = "1"
 MINPACK = "1"
 MadNLP = "0.8"
+MadNLPMumps = "0.5"
 NLPModelsIpopt = "0.10"
 NLPModelsKnitro = "0.9"
 NonlinearSolve = "4"

diff --git a/docs/make.jl b/docs/make.jl
@@ -14,6 +14,7 @@ using ADNLPModels
 using ExaModels
 using NLPModelsIpopt
 using MadNLP
+using MadNLPMumps
 using JSON3
 using JLD2
 using NLPModelsKnitro

diff --git a/docs/src/api-optimalcontrol-user.md b/docs/src/api-optimalcontrol-user.md
@@ -79,9 +79,10 @@ iterations
 lagrange
 mayer
 message
-model
+nlp_model
 objective(::Model)
 objective(::Solution)
+ocp_model
 plot(::Solution, ::Symbol...)
 plot!(::Plots.Plot, ::Solution, ::Symbol...)
 set_initial_guess

diff --git a/docs/src/assets/Manifest.toml b/docs/src/assets/Manifest.toml
diff --git a/docs/src/assets/Project.toml b/docs/src/assets/Project.toml
@@ -18,6 +18,7 @@ JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
+MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
@@ -29,7 +30,7 @@ Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 [compat]
 ADNLPModels = "0.8"
 CTBase = "0.16"
-CTDirect = "0.16"
+CTDirect = "0.17"
 CTFlows = "0.8"
 CTModels = "0.6"
 CTParser = "0.6"
@@ -46,6 +47,7 @@ JSON3 = "1"
 LinearAlgebra = "1"
 MINPACK = "1"
 MadNLP = "0.8"
+MadNLPMumps = "0.5"
 NLPModelsIpopt = "0.10"
 NLPModelsKnitro = "0.9"
 NonlinearSolve = "4"

diff --git a/docs/src/manual-solve.md b/docs/src/manual-solve.md
@@ -107,7 +107,7 @@ ocp = @def begin
     0.5∫( u(t)^2 ) → min
 end
 
-solve(ocp, :exa, :madnlp)
+solve(ocp, :exa, :madnlp; disc_method=:trapeze)
 nothing # hide
 ```
 
@@ -133,7 +133,7 @@ The main options for the direct method, with their [default] values, are:
   More precisely, if `N = grid_size` and the initial and final times are `t0` and `tf`, then the step length `Δt = (tf - t0) / N`.
 - `time_grid` ([`nothing`]): explicit time grid (can be non-uniform).  
   If `time_grid = nothing`, a uniform grid of length `grid_size` is used.
-- `disc_method` ([`:trapeze`], `:midpoint`, `:euler`, `:euler_implicit`, `:gauss_legendre_2`, `:gauss_legendre_3`): the discretisation scheme to transform the dynamics into nonlinear equations. See the [discretization method tutorial](https://control-toolbox.org/Tutorials.jl/stable/tutorial-discretisation.html) for more details.
+- `disc_method` (`:trapeze`, [`:midpoint`], `:euler`, `:euler_implicit`, `:gauss_legendre_2`, `:gauss_legendre_3`): the discretisation scheme to transform the dynamics into nonlinear equations. See the [discretization method tutorial](https://control-toolbox.org/Tutorials.jl/stable/tutorial-discretisation.html) for more details.
 - `adnlp_backend` ([`:optimized`], `:manual`, `:default`): backend used for automatic differentiation to create the [`ADNLPModels.ADNLPModel`](@extref).
 
 For advanced usage, see:

diff --git a/src/OptimalControl.jl b/src/OptimalControl.jl
@@ -136,8 +136,8 @@ export constraints,
 
 # CTDirect
 import CTDirect:
-    CTDirect, direct_transcription, set_initial_guess, build_OCP_solution, model
-export direct_transcription, set_initial_guess, build_OCP_solution, model
+    CTDirect, direct_transcription, set_initial_guess, build_OCP_solution, nlp_model, ocp_model
+export direct_transcription, set_initial_guess, build_OCP_solution, nlp_model, ocp_model
 
 # CTFlows
 import CTFlows:

diff --git a/test/Project.toml b/test/Project.toml
@@ -5,6 +5,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
+MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
@@ -17,6 +18,7 @@ ForwardDiff = "0.10, 1.0"
 LinearAlgebra = "1"
 MINPACK = "1"
 MadNLP = "0.8"
+MadNLPMumps = "0.5"
 NLPModelsIpopt = "0.10"
 OrdinaryDiffEq = "6"
 SplitApplyCombine = "1"

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -2,6 +2,7 @@ using Test
 using OptimalControl
 using NLPModelsIpopt
 using MadNLP
+using MadNLPMumps
 using LinearAlgebra
 using OrdinaryDiffEq
 using MINPACK