added: @warn about random values in getinfo!

The sparsity structure is still a useful information IMO, so it is worth it to output the matrices anyway with this warning

Author: franckgaga

src/controller/execute.jl

@@ -117,6 +117,9 @@ are also available:
 - `:∇geq` or *`:nablageq`* : Jacobian of the equality constraint, ``\mathbf{\nabla g_{eq}}``
 - `:∇²ℓgeq` or *`:nabla2lgeq`* : Hessian of the equality Lagrangian, ``\mathbf{\nabla^2}\ell_{\mathbf{g_{eq}}}``
 
+Note that Hessian of Lagrangians are not fully supported yet. Their nonzero coefficients are
+random values for now.
+
 # Examples
 ```jldoctest
 julia> mpc = LinMPC(LinModel(tf(5, [2, 1]), 3), Nwt=[0], Hp=1, Hc=1);

src/controller/nonlinmpc.jl

@@ -586,7 +586,11 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
         return nothing
     end
     ∇g = jacobian(g!, g, mpc.jacobian, mpc.Z̃, ∇g_cache...)
-    if !isnothing(mpc.hessian) && any(old_i_g) 
+    if !isnothing(mpc.hessian) && any(old_i_g)
+        @warn(
+            "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
+            "Its nonzero coefficients are random values for now.", maxlog=1
+        )
         function ℓ_g(Z̃, λ, ΔŨ, x̂0end, Ue, Ŷe, U0, Ŷ0, Û0, K0, X̂0, gc, geq, g)
             update_predictions!(ΔŨ, x̂0end, Ue, Ŷe, U0, Ŷ0, Û0, K0, X̂0, gc, g, geq, mpc, Z̃)
             return dot(λ, g)
@@ -598,7 +602,7 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
         )
         nonlincon = optim[:nonlinconstraint]
         λ = JuMP.dual.(nonlincon) # FIXME: does not work for now
-        λ = ones(NT, ng)
+        λ = rand(NT, ng)
         ∇²ℓg = hessian(ℓ_g, mpc.hessian, mpc.Z̃, Constant(λ), ∇²g_cache...)
     else
         ∇²ℓg = nothing
@@ -616,6 +620,10 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
     end
     ∇geq = jacobian(geq!, geq, mpc.jacobian, mpc.Z̃, geq_cache...)
     if !isnothing(mpc.hessian) && con.neq > 0
+        @warn(
+            "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
+            "Its nonzero coefficients are random values for now.", maxlog=1
+        )
         ∇²geq_cache = (
             Cache(ΔŨ), Cache(x̂0end), Cache(Ue), Cache(Ŷe), Cache(U0), Cache(Ŷ0),
             Cache(Û0), Cache(K0),   Cache(X̂0),

src/estimator/mhe/execute.jl

@@ -103,6 +103,9 @@ For [`NonLinModel`](@ref), it also includes the following derivative fields:
 - `:∇g` or *`:nablag`* : Jacobian of the inequality constraint, ``\mathbf{\nabla g}``
 - `:∇²ℓg` or *`:nabla2lg`* : Hessian of the inequality Lagrangian, ``\mathbf{\nabla^2}\ell_{\mathbf{g}}``
 
+Note that Hessian of Lagrangians are not fully supported yet. Their nonzero coefficients are
+random values for now.
+
 # Examples
 ```jldoctest
 julia> model = LinModel(ss(1.0, 1.0, 1.0, 0, 5.0));
@@ -218,7 +221,11 @@ function addinfo!(
         return nothing
     end
     ∇g = jacobian(g!, g, estim.jacobian, estim.Z̃, ∇g_cache...)
-    if !isnothing(estim.hessian) && any(old_i_g) 
+    if !isnothing(estim.hessian) && any(old_i_g)
+        @warn(
+            "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
+            "Its nonzero coefficients are random values for now.", maxlog=1
+        )
         ∇²g_cache = (Cache(V̂), Cache(X̂0), Cache(û0), Cache(k0), Cache(ŷ0), Cache(g))
         function ℓ_g(Z̃, λ, V̂, X̂0, û0, k0, ŷ0, g)
             update_prediction!(V̂, X̂0, û0, k0, ŷ0, g, estim, Z̃)

test/2_test_state_estim.jl

@@ -967,6 +967,7 @@ end
     @test info[:Ŷ][end-1:end] ≈ [50, 30] atol=1e-9
 
     mhe1c = MovingHorizonEstimator(nonlinmodel, He=2, direct=false, hessian=true)
+    mhe1c = setconstraint!(mhe1c, v̂min = [-1000, -1000], v̂max = [1000, 1000]) # coverage of getinfo Hessian of Lagrangian
     preparestate!(mhe1c, [50, 30], [5])
     x̂ = updatestate!(mhe1c, [10, 50], [50, 30], [5])
     @test x̂ ≈ zeros(6) atol=1e-9

test/3_test_predictive_control.jl

@@ -814,7 +814,6 @@ end
     preparestate!(nmpc4, [0], [0])
     u = moveinput!(nmpc4, [0], d, R̂u=fill(12, nmpc4.Hp))
     @test u ≈ [12] atol=5e-2
-    linmodel3 = LinModel{Float32}(0.5*ones(1,1), ones(1,1), ones(1,1), 0, 0, 3000.0)
     nmpc5 = NonLinMPC(nonlinmodel, Hp=1, Hc=1, Cwt=Inf, transcription=MultipleShooting())
     nmpc5 = setconstraint!(nmpc5, ymin=[1])
     f! = (ẋ,x,u,_,_) -> ẋ .= -0.001x .+ u 
@@ -830,6 +829,7 @@ end
     preparestate!(nmpc5_1, [0.0])
     u = moveinput!(nmpc5_1, [1/0.001])
     @test u ≈ [1.0] atol=5e-2
+    linmodel3 = LinModel{Float32}(0.5*ones(1,1), ones(1,1), ones(1,1), 0, 0, 3000.0)
     nmpc6  = NonLinMPC(linmodel3, Hp=10)
     preparestate!(nmpc6, [0])
     @test moveinput!(nmpc6, [0]) ≈ [0.0] atol=5e-2
@@ -849,6 +849,7 @@ end
     @test info[:Ŷ][end] ≈ 10 atol=5e-2
     transcription = MultipleShooting(f_threads=true, h_threads=true)
     nmpc8t = NonLinMPC(nonlinmodel; Nwt=[0], Hp=100, Hc=1, transcription)
+    nmpc8t = setconstraint!(nmpc8t, ymax=[100], ymin=[-100]) # coverage of getinfo! Hessians of Lagrangian
     preparestate!(nmpc8t, [0], [0])
     u = moveinput!(nmpc8t, [10], [0])
     @test u ≈ [2] atol=5e-2