fix generation functions, tests now pass

Author: ChrisRackauckas

src/systems/optimization/optimizationsystem.jl

@@ -86,6 +86,9 @@ hessian_sparsity(sys::OptimizationSystem) =
 
 struct AutoModelingToolkit <: DiffEqBase.AbstractADType end
 
+DiffEqBase.OptimizationProblem(sys::OptimizationSystem,args...;kwargs...) =
+    DiffEqBase.OptimizationProblem{true}(sys::OptimizationSystem,args...;kwargs...)
+
 """
 ```julia
 function DiffEqBase.OptimizationProblem{iip}(sys::OptimizationSystem,
@@ -120,7 +123,7 @@ function DiffEqBase.OptimizationProblem{iip}(sys::OptimizationSystem, u0,
         grad_oop,grad_iip = generate_gradient(sys,checkbounds=checkbounds,linenumbers=linenumbers,
                                   parallel=parallel,expression=Val{false})
         _grad(u,p) = grad_oop(u,p)
-        _grad(J,u,p) = grad_iip(J,u,p)
+        _grad(J,u,p) = (grad_iip(J,u,p); J)
     else
         _grad = nothing
     end
@@ -129,7 +132,7 @@ function DiffEqBase.OptimizationProblem{iip}(sys::OptimizationSystem, u0,
         hess_oop,hess_iip = generate_hessian(sys,checkbounds=checkbounds,linenumbers=linenumbers,
                                  sparse=sparse,parallel=parallel,expression=Val{false})
        _hess(u,p) = hess_oop(u,p)
-       _hess(J,u,p) = hess_iip(J,u,p)
+       _hess(J,u,p) = (hess_iip(J,u,p); J)
     else
         _hess = nothing
     end

test/modelingtoolkitize.jl

@@ -49,12 +49,12 @@ p  = [1.0,100.0]
 
 prob = OptimizationProblem(rosenbrock,x0,p)
 sys = modelingtoolkitize(prob)
+x0map = states(sys) .=> x0
+parammap = parameters(sys) .=> p
 
-prob = OptimizationProblem(ModelingToolkit.OptimizationFunction(
-                    rosenbrock,x0,ModelingToolkit.AutoModelingToolkit(),p,
-                                     grad = true), x0,p)
+prob = OptimizationProblem(sys,x0map,parammap,grad=true)
 sol = solve(prob,NelderMead())
 @test sol.minimum < 1e-8
 
 sol = solve(prob,BFGS())
-@test_broken sol.minimum < 1e-8
+@test sol.minimum < 1e-8

test/optimizationsystem.jl

@@ -1,4 +1,4 @@
-using ModelingToolkit, SparseArrays
+using ModelingToolkit, SparseArrays, Test, GalacticOptim, Optim
 
 @variables x y
 @parameters a b
@@ -36,7 +36,13 @@ p = [
     sys2.b => 9.0
     β => 10.0
 ]
+
 prob = OptimizationProblem(combinedsys,u0,p,grad=true)
+sol = solve(prob,NelderMead())
+@test sol.minimum < -1e5
 
-using GalacticOptim, Optim
-solve(prob,BFGS())
+prob2 = remake(prob,u0=sol.minimizer)
+sol = solve(prob,BFGS(initial_stepnorm=0.0001),allow_f_increases=true)
+@test sol.minimum < -1e8
+sol = solve(prob2,BFGS(initial_stepnorm=0.0001),allow_f_increases=true)
+@test sol.minimum < -1e9