add back mtkext

ext/OptimizationMTKExt.jl

@@ -0,0 +1,102 @@
+module OptimizationMTKExt
+
+import OptimizationBase, OptimizationBase.ArrayInterface
+import OptimizationBase.SciMLBase
+import OptimizationBase.SciMLBase: OptimizationFunction
+import OptimizationBase.ADTypes: AutoModelingToolkit
+isdefined(Base, :get_extension) ? (using ModelingToolkit) : (using ..ModelingToolkit)
+
+function OptimizationBase.instantiate_function(f, x, adtype::AutoModelingToolkit, p,
+        num_cons = 0)
+    p = isnothing(p) ? SciMLBase.NullParameters() : p
+
+    sys = complete(ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, x, p;
+        lcons = fill(0.0,
+            num_cons),
+        ucons = fill(0.0,
+            num_cons))))
+    #sys = ModelingToolkit.structural_simplify(sys)
+    f = OptimizationProblem(sys, x, p, grad = true, hess = true,
+        sparse = adtype.obj_sparse, cons_j = true, cons_h = true,
+        cons_sparse = adtype.cons_sparse).f
+
+    grad = (G, θ, args...) -> f.grad(G, θ, p, args...)
+
+    hess = (H, θ, args...) -> f.hess(H, θ, p, args...)
+
+    hv = function (H, θ, v, args...)
+        res = adtype.obj_sparse ? (eltype(θ)).(f.hess_prototype) :
+              ArrayInterface.zeromatrix(θ)
+        hess(res, θ, args...)
+        H .= res * v
+    end
+
+    if !isnothing(f.cons)
+        cons = (res, θ) -> f.cons(res, θ, p)
+        cons_j = (J, θ) -> f.cons_j(J, θ, p)
+        cons_h = (res, θ) -> f.cons_h(res, θ, p)
+    else
+        cons = nothing
+        cons_j = nothing
+        cons_h = nothing
+    end
+
+    return OptimizationFunction{true}(f.f, adtype; grad = grad, hess = hess, hv = hv,
+        cons = cons, cons_j = cons_j, cons_h = cons_h,
+        hess_prototype = f.hess_prototype,
+        cons_jac_prototype = f.cons_jac_prototype,
+        cons_hess_prototype = f.cons_hess_prototype,
+        expr = OptimizationBase.symbolify(f.expr),
+        cons_expr = OptimizationBase.symbolify.(f.cons_expr),
+        sys = sys,
+        observed = f.observed)
+end
+
+function OptimizationBase.instantiate_function(f, cache::OptimizationBase.ReInitCache,
+        adtype::AutoModelingToolkit, num_cons = 0)
+    p = isnothing(cache.p) ? SciMLBase.NullParameters() : cache.p
+
+    sys = complete(ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, cache.u0,
+        cache.p;
+        lcons = fill(0.0,
+            num_cons),
+        ucons = fill(0.0,
+            num_cons))))
+    #sys = ModelingToolkit.structural_simplify(sys)
+    f = OptimizationProblem(sys, cache.u0, cache.p, grad = true, hess = true,
+        sparse = adtype.obj_sparse, cons_j = true, cons_h = true,
+        cons_sparse = adtype.cons_sparse).f
+
+    grad = (G, θ, args...) -> f.grad(G, θ, cache.p, args...)
+
+    hess = (H, θ, args...) -> f.hess(H, θ, cache.p, args...)
+
+    hv = function (H, θ, v, args...)
+        res = adtype.obj_sparse ? (eltype(θ)).(f.hess_prototype) :
+              ArrayInterface.zeromatrix(θ)
+        hess(res, θ, args...)
+        H .= res * v
+    end
+
+    if !isnothing(f.cons)
+        cons = (res, θ) -> f.cons(res, θ, cache.p)
+        cons_j = (J, θ) -> f.cons_j(J, θ, cache.p)
+        cons_h = (res, θ) -> f.cons_h(res, θ, cache.p)
+    else
+        cons = nothing
+        cons_j = nothing
+        cons_h = nothing
+    end
+
+    return OptimizationFunction{true}(f.f, adtype; grad = grad, hess = hess, hv = hv,
+        cons = cons, cons_j = cons_j, cons_h = cons_h,
+        hess_prototype = f.hess_prototype,
+        cons_jac_prototype = f.cons_jac_prototype,
+        cons_hess_prototype = f.cons_hess_prototype,
+        expr = OptimizationBase.symbolify(f.expr),
+        cons_expr = OptimizationBase.symbolify.(f.cons_expr),
+        sys = sys,
+        observed = f.observed)
+end
+
+end