[Bug] Max objective for exa

Project.toml

@@ -1,14 +1,19 @@
 name = "CTDirect"
 uuid = "790bbbee-bee9-49ee-8912-a9de031322d5"
+version = "0.18.0"
 authors = ["Pierre Martinon <pierrecmartinon@gmail.com>"]
-version = "0.17.3"
+
+[workspace]
+projects = ["test", "docs"]
 
 [deps]
 CTBase = "54762871-cc72-4466-b8e8-f6c8b58076cd"
 CTModels = "34c4fa32-2049-4079-8329-de33c2a22e2d"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 HSL = "34c5aeac-e683-54a6-a0e9-6e0fdc586c50"
 MKL = "33e6dc65-8f57-5167-99aa-e5a354878fb2"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [weakdeps]
@@ -38,8 +43,10 @@ MKL = "0.9"
 MadNLP = "0.8"
 MadNLPGPU = "0.7"
 MadNLPMumps = "0.5"
+NLPModels = "0.21"
 NLPModelsIpopt = "0.11"
 NLPModelsKnitro = "0.9"
+SolverCore = "0.3.8"
 SplitApplyCombine = "1"
 julia = "1.10"
 
@@ -49,11 +56,9 @@ CTParser = "32681960-a1b1-40db-9bff-a1ca817385d1"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 MadNLPGPU = "d72a61cc-809d-412f-99be-fd81f4b8a598"
 MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "SplitApplyCombine", "CTParser", "ADNLPModels", "ExaModels", "NLPModelsIpopt", "MadNLPMumps", "MadNLPGPU", "CUDA", "AMDGPU"]
-
-[workspace]
-projects = ["test", "docs"]
+test = ["Test", "SplitApplyCombine", "CTParser", "ADNLPModels", "ExaModels", "NLPModels", "NLPModelsIpopt", "MadNLPMumps", "MadNLPGPU", "CUDA", "AMDGPU"]

ext/CTDirectExtADNLP.jl

@@ -1,10 +1,9 @@
 module CTDirectExtADNLP
 
 using CTDirect
-
 using DocStringExtensions
-
 using ADNLPModels
+using CTModels
 
 """
 $(TYPEDSIGNATURES)
@@ -16,8 +15,11 @@ Build the NLP model for the DOCP (ADNLPModels version)
 * `adnlp_backend`: backend for ADNLPModels ([`:optimized`], `:manual`, `:default`)
 """
 function CTDirect.build_nlp!(
-    docp::CTDirect.DOCP,
-    nlp_model::CTDirect.ADNLPBackend,
+    docp::CTDirect.DOCP{
+        <:CTDirect.Discretization,
+        <:CTModels.Model,
+        <:CTDirect.ADNLPBackend,
+    },
     x0;
     adnlp_backend=CTDirect.__adnlp_backend(),
     show_time=false, #+default

ext/CTDirectExtExa.jl

@@ -2,10 +2,9 @@ module CTDirectExtExa
 
 using CTDirect
 using CTModels: CTModels
-
 using DocStringExtensions
-
 using ExaModels
+using SolverCore
 
 """
 $(TYPEDSIGNATURES)
@@ -18,8 +17,11 @@ Build the NLP model for the DOCP (ExaModels version)
 * `exa_backend`: backend for ExaModels ([`nothing`])
 """
 function CTDirect.build_nlp!(
-    docp::CTDirect.DOCP,
-    nlp_model::CTDirect.ExaBackend,
+    docp::CTDirect.DOCP{
+        <:CTDirect.Discretization,
+        <:CTModels.Model,
+        <:CTDirect.ExaBackend,
+    },
     x0;
     grid_size=CTDirect.__grid_size(),
     disc_method=CTDirect.__disc_method(),
@@ -60,12 +62,26 @@ function CTDirect.build_nlp!(
     return nothing
 end
 
-function CTDirect.get_time_grid_exa(docp_solution, docp)
+"""
+$(TYPEDSIGNATURES)
+
+Retrieve the time grid from the given DOCP solution.
+
+# Arguments
+
+- `nlp_solution`: The DOCP solution.
+- `docp`: The DOCP.
+
+# Returns
+
+- `::Vector{Float64}`: The time grid.
+"""
+function CTDirect.get_time_grid_exa(nlp_solution::SolverCore.AbstractExecutionStats, docp::CTDirect.DOCP)
     grid = zeros(docp.time.steps+1)
     ocp = docp.ocp
 
     if docp.flags.freet0 || docp.flags.freetf
-        v = docp.exa_getter(docp_solution; val=:variable)
+        v = docp.exa_getter(nlp_solution; val=:variable)
     end
 
     if docp.flags.freet0

ext/CTDirectExtIpopt.jl

@@ -35,7 +35,7 @@ $(TYPEDSIGNATURES)
 Solve a discretized optimal control problem (Ipopt version).
 """
 function CTDirect.solve_docp(
-    solver_backend::CTDirect.IpoptBackend,
+    ::CTDirect.IpoptBackend,
     docp::CTDirect.DOCP;
     display::Bool=CTDirect.__display(),
     max_iter::Integer=CTDirect.__max_iterations(),
@@ -75,7 +75,7 @@ function CTDirect.solve_docp(
     solver = IpoptSolver(nlp)
 
     # solve discretized problem with NLP solver
-    docp_solution = solve!(
+    nlp_solution = solve!(
         solver,
         nlp;
         print_level=print_level,
@@ -88,8 +88,8 @@ function CTDirect.solve_docp(
         kwargs...,
     )
 
-    # return DOCP solution
-    return docp_solution
+    # return NLP solution
+    return nlp_solution
 end
 
 end

ext/CTDirectExtKnitro.jl

@@ -21,7 +21,7 @@ $(TYPEDSIGNATURES)
 Solve a discretized optimal control problem with Ipopt
 """
 function CTDirect.solve_docp(
-    solver_backend::CTDirect.KnitroBackend,
+    ::CTDirect.KnitroBackend,
     docp::CTDirect.DOCP;
     display::Bool=CTDirect.__display(),
     max_iter::Integer=CTDirect.__max_iterations(),
@@ -44,10 +44,10 @@ function CTDirect.solve_docp(
     )
 
     # solve discretized problem with NLP solver
-    docp_solution = solve!(solver, nlp)
+    nlp_solution = solve!(solver, nlp)
 
-    # return DOCP solution
-    return docp_solution
+    # return NLP solution
+    return nlp_solution
 end
 
 end

ext/CTDirectExtMadNLP.jl

@@ -7,6 +7,7 @@ using DocStringExtensions
 using MadNLP
 using HSL
 using MKL
+using NLPModels
 
 """
 $(TYPEDSIGNATURES)
@@ -22,7 +23,7 @@ $(TYPEDSIGNATURES)
 Solve a discretized optimal control problem DOCP
 """
 function CTDirect.solve_docp(
-    solver_backend::CTDirect.MadNLPBackend,
+    ::CTDirect.MadNLPBackend,
     docp::CTDirect.DOCP;
     display::Bool=CTDirect.__display(),
     max_iter::Integer=CTDirect.__max_iterations(),
@@ -43,19 +44,19 @@ function CTDirect.solve_docp(
     )
 
     # solve discretized problem with NLP solver
-    docp_solution = solve!(solver)
+    nlp_solution = solve!(solver)
 
-    # return DOCP solution
-    return docp_solution
+    # return NLP solution
+    return nlp_solution
 end
 
-function CTDirect.SolverInfos(nlp_solution::MadNLP.MadNLPExecutionStats)
-    objective = nlp_solution.objective # NB sign is incorrect for max problems !
+function CTDirect.SolverInfos(nlp_solution::MadNLP.MadNLPExecutionStats, nlp::NLPModels.AbstractNLPModel)
+    minimize = NLPModels.get_minimize(nlp)
+    objective = minimize ? nlp_solution.objective : -nlp_solution.objective # sign depends on minimization for MadNLP
     iterations = nlp_solution.iter
     constraints_violation = nlp_solution.primal_feas
     status = Symbol(nlp_solution.status)
     successful = (status == :SOLVE_SUCCEEDED) || (status == :SOLVED_TO_ACCEPTABLE_LEVEL)
-
     return objective, iterations, constraints_violation, "MadNLP", status, successful
 end
 

src/CTDirect.jl

@@ -4,6 +4,8 @@ using CTBase
 using CTModels: CTModels
 using DocStringExtensions
 using SparseArrays
+using SolverCore: SolverCore
+using NLPModels: NLPModels
 
 # ----------------------------------------------------------------------
 # EXTENSIONS

src/docp.jl

@@ -277,7 +277,7 @@ Struct representing a discretized optimal control problem (DOCP).
 
 - `discretization::D`: The discretization scheme.
 - `ocp::O`: The original OCP model.
-- `nlp_model::N`: The NLP model backend.
+- `nlp_model_backend::N`: The NLP model backend.
 - `nlp`: The constructed NLP instance.
 - `exa_getter::Union{Nothing,Function}`: Getter for ExaModels if used.
 - `flags::DOCPFlags`: Boolean flags describing problem structure.
@@ -290,11 +290,11 @@ Struct representing a discretized optimal control problem (DOCP).
 # Example
 
 ```julia-repl
-julia> DOCP(ocp, nlp_model)
+julia> DOCP(ocp, nlp_model_backend)
 DOCP{...}(...)
 ```
 """
-mutable struct DOCP{D<:Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPModelBackend}
+mutable struct DOCP{D<:CTDirect.Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPModelBackend}
 
     # discretization scheme
     discretization::D
@@ -303,7 +303,7 @@ mutable struct DOCP{D<:Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPM
     ocp::O # parametric instead of just qualifying reduces allocations (but not time). Specialization ?
 
     # NLP
-    nlp_model::N
+    nlp_model_backend::N
     nlp
     exa_getter::Union{Nothing,Function} # getter for ExaModels (if used)
 
@@ -326,7 +326,7 @@ mutable struct DOCP{D<:Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPM
     # constructor
     function DOCP(
         ocp::CTModels.Model,
-        nlp_model;
+        nlp_model_backend::CTDirect.AbstractNLPModelBackend;
         grid_size=__grid_size(),
         time_grid=__time_grid(),
         disc_method=__disc_method(),
@@ -423,10 +423,10 @@ mutable struct DOCP{D<:Discretization,O<:CTModels.Model,N<:CTDirect.AbstractNLPM
         )
 
         # call constructor with const fields
-        docp = new{typeof(discretization),typeof(ocp),typeof(nlp_model)}(
+        docp = new{typeof(discretization),typeof(ocp),typeof(nlp_model_backend)}(
             discretization,
             ocp,
-            nlp_model,
+            nlp_model_backend,
             nothing, # nlp
             nothing, # exa_getter
             flags,