added: using strict=Val(true) un DI.jl preparation

Author: franckgaga

Project.toml

@@ -22,7 +22,7 @@ SparseMatrixColorings = "0a514795-09f3-496d-8182-132a7b665d35"
 
 [compat]
 ControlSystemsBase = "1.9"
-DifferentiationInterface = "0.6.44"
+DifferentiationInterface = "0.6.45"
 ForwardDiff = "0.10"
 Ipopt = "1"
 JuMP = "1.21"

src/controller/nonlinmpc.jl

@@ -608,6 +608,7 @@ function get_optim_functions(
     ng, nc, neq = length(mpc.con.i_g), mpc.con.nc, mpc.con.neq
     nZ̃, nU, nŶ, nX̂ = length(mpc.Z̃), Hp*nu, Hp*ny, Hp*nx̂
     nΔŨ, nUe, nŶe = nu*Hc + nϵ, nU + nu, nŶ + ny  
+    strict = Val(true)
     myNaN  = convert(JNT, NaN)  # NaN to force update_simulations! at first call:
     Z̃ ::Vector{JNT}                  = fill(myNaN, nZ̃)
     ΔŨ::Vector{JNT}                  = zeros(JNT, nΔŨ)
@@ -636,7 +637,7 @@ function get_optim_functions(
         Cache(Û0), Cache(X̂0), 
         Cache(gc), Cache(g), Cache(geq),
     )
-    ∇J_prep = prepare_gradient(Jfunc!, grad_backend, Z̃_∇J, ∇J_context...)
+    ∇J_prep = prepare_gradient(Jfunc!, grad_backend, Z̃_∇J, ∇J_context...; strict)
     ∇J = Vector{JNT}(undef, nZ̃)
     ∇Jfunc! = if nZ̃ == 1
         function (Z̃arg)
@@ -675,7 +676,7 @@ function get_optim_functions(
     )
     # temporarily enable all the inequality constraints for sparsity detection:
     mpc.con.i_g[1:end-nc] .= true
-    ∇g_prep  = prepare_jacobian(gfunc!, g, jac_backend, Z̃_∇g, ∇g_context...)
+    ∇g_prep  = prepare_jacobian(gfunc!, g, jac_backend, Z̃_∇g, ∇g_context...; strict)
     mpc.con.i_g[1:end-nc] .= false
     ∇g = init_diffmat(JNT, jac_backend, ∇g_prep, nZ̃, ng)
     ∇gfuncs! = Vector{Function}(undef, ng)
@@ -721,7 +722,7 @@ function get_optim_functions(
         Cache(Û0), Cache(X̂0),
         Cache(gc), Cache(g)
     )
-    ∇geq_prep  = prepare_jacobian(geqfunc!, geq, jac_backend, Z̃_∇geq, ∇geq_context...)
+    ∇geq_prep = prepare_jacobian(geqfunc!, geq, jac_backend, Z̃_∇geq, ∇geq_context...; strict)
     ∇geq = init_diffmat(JNT, jac_backend, ∇geq_prep, nZ̃, neq)
     ∇geqfuncs! = Vector{Function}(undef, neq)
     for i in eachindex(∇geqfuncs!)

src/estimator/mhe/construct.jl

@@ -1345,6 +1345,7 @@ function get_optim_functions(
     nx̂, nym, nŷ, nu, nϵ, He = estim.nx̂, estim.nym, model.ny, model.nu, estim.nϵ, estim.He
     nV̂, nX̂, ng, nZ̃ = He*nym, He*nx̂, length(con.i_g), length(estim.Z̃)
     myNaN = convert(JNT, NaN) # NaN to force update_simulations! at first call
+    strict = Val(true)
     Z̃::Vector{JNT}                   = fill(myNaN, nZ̃) 
     V̂::Vector{JNT},  X̂0::Vector{JNT} = zeros(JNT, nV̂), zeros(JNT, nX̂)
     û0::Vector{JNT}, ŷ0::Vector{JNT} = zeros(JNT, nu), zeros(JNT, nŷ)
@@ -1370,7 +1371,7 @@ function get_optim_functions(
         Cache(g),
         Cache(x̄),
     )
-    ∇J_prep = prepare_gradient(Jfunc!, grad_backend, Z̃_∇J, ∇J_context...)
+    ∇J_prep = prepare_gradient(Jfunc!, grad_backend, Z̃_∇J, ∇J_context...; strict)
     ∇J = Vector{JNT}(undef, nZ̃)
     ∇Jfunc! = if nZ̃ == 1
         function (Z̃arg) 
@@ -1409,7 +1410,7 @@ function get_optim_functions(
     # temporarily enable all the inequality constraints for sparsity detection:
     estim.con.i_g .= true  
     estim.Nk[] = He
-    ∇g_prep  = prepare_jacobian(gfunc!, g, jac_backend, Z̃_∇g, ∇g_context...)
+    ∇g_prep  = prepare_jacobian(gfunc!, g, jac_backend, Z̃_∇g, ∇g_context...; strict)
     estim.con.i_g .= false
     estim.Nk[] = 0
     ∇g = init_diffmat(JNT, jac_backend, ∇g_prep, nZ̃, ng)