ADD: test example works for Enzyme only, but failed optimization

test/testoptimization.jl

@@ -1,14 +1,11 @@
-module enzymeDiff
-
+using Enzyme
 using DJUICE
 using MAT
 using Test
-using Enzyme
 
 using Optimization, OptimizationOptimJL
 
 #Load model from MATLAB file
-#file = matopen(joinpath(@__DIR__, "..", "data","temp12k.mat")) #BIG model
 file = matopen(joinpath(@__DIR__, "..", "data","temp.mat")) #SMALL model (35 elements)
 mat  = read(file, "md")
 close(file)
@@ -19,27 +16,27 @@ md.stressbalance.maxiter = 20
 
 #Now call AD!
 md.inversion.iscontrol = 1
+md.inversion.onlygrad = 1
 md.inversion.independent = md.friction.coefficient
 md.inversion.min_parameters = ones(md.mesh.numberofvertices)*(0.0)
 md.inversion.max_parameters = ones(md.mesh.numberofvertices)*(1.0e3)
 md.inversion.independent_string = "FrictionCoefficient"
+md.inversion.dependent_string = ["SurfaceAbsVelMisfit"]
 
 α = md.inversion.independent
 ∂J_∂α = zero(α)
 
 femmodel=DJUICE.ModelProcessor(md, :StressbalanceSolution)
 n = length(α)
+
+# test Enzyme autodiff only
+dfemmodel = Enzyme.Compiler.make_zero(Base.Core.Typeof(femmodel), IdDict(), femmodel)
+autodiff(Enzyme.Reverse, DJUICE.costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
+
 # use user defined grad, errors!
-optprob = OptimizationFunction(DJUICE.costfunction, Optimization.AutoEnzyme(), grad=DJUICE.computeGradient)
-#optprob = OptimizationFunction(DJUICE.costfunction, Optimization.AutoEnzyme())
+optprob = OptimizationFunction(DJUICE.costfunction, Optimization.AutoEnzyme())
 #prob = Optimization.OptimizationProblem(optprob, α, femmodel, lb=md.inversion.min_parameters, ub=md.inversion.max_parameters)
 prob = Optimization.OptimizationProblem(optprob, α, femmodel)
 sol = Optimization.solve(prob, Optim.LBFGS())
-
-
-#md = solve(md, :sb)
-
-# compute gradient by finite differences at each node
-#addJ = md.results["StressbalanceSolution"]["Gradient"]
-
-end
+#sol = Optimization.solve(prob, Optim.GradientDescent())
+#sol = Optimization.solve(prob, Optim.NelderMead())