diff --git a/Project.toml b/Project.toml
index af7a5ba..f07a7a9 100644
--- a/Project.toml
+++ b/Project.toml
@@ -4,11 +4,15 @@ authors = ["Mathieu Morlighem <Mathieu.Morlighem@dartmouth.edu>", "Gong Cheng <g
 version = "0.1.1"
 
 [deps]
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
 MAT = "23992714-dd62-5051-b70f-ba57cb901cac"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
@@ -16,14 +20,14 @@ Triangulate = "f7e6ffb2-c36d-4f8f-a77e-16e897189344"
 
 [compat]
 ColorSchemes = "3.25"
-Enzyme = "0.12"
+Enzyme = "0.13"
 Flux = "0.14"
 MAT = "0.10"
 Makie = "0.21"
-SparseArrays = "1.8.0"
+SparseArrays = "1"
 StatsBase = "0.34"
 Triangulate = "2.3"
-julia = "1.8"
+julia = "1.10"
 
 [extras]
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
diff --git a/src/core/control.jl b/src/core/control.jl
index fbc0d23..4a6df06 100644
--- a/src/core/control.jl
+++ b/src/core/control.jl
@@ -29,7 +29,7 @@ function ComputeGradient(∂J_∂α::Vector{Float64}, α::Vector{Float64}, femmo
 	# zero ALL depth of the model, make sure we get correct gradient
 	dfemmodel = Enzyme.Compiler.make_zero(Base.Core.Typeof(femmodel), IdDict(), femmodel)
 	# compute the gradient
-	autodiff(Enzyme.Reverse, costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
+	autodiff(set_runtime_activity(Enzyme.Reverse), costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
 end#}}}
 function CostFunctionx(femmodel::FemModel, α::Vector{Float64}, controlvar_enum::IssmEnum, SId_enum::IssmEnum, cost_enum_list::Vector{IssmEnum}, ::Val{solutionstring}) where solutionstring #{{{
 	#Update FemModel accordingly
@@ -61,7 +61,7 @@ end#}}}
 
 # cost function handler for autodiff
 function costfunction(α::Vector{Float64}, femmodel::FemModel) #{{{
-	# get the md.inversion.control_string
+	# get the md.inversion.independent_string
 	control_string = FindParam(String, femmodel.parameters, InversionControlParametersEnum)
 	# get the Enum
 	controlvar_enum = StringToEnum(control_string)
@@ -71,7 +71,10 @@ function costfunction(α::Vector{Float64}, femmodel::FemModel) #{{{
 
 	# get the cost function list from md.inversion.dependent_string
 	cost_list = FindParam(Vector{String}, femmodel.parameters, InversionCostFunctionsEnum)
-	cost_enum_list = map(StringToEnum, cost_list)
+	cost_enum_list = Vector{IssmEnum}(undef, length(cost_list))
+	for (index, value) in enumerate(cost_list)
+		cost_enum_list[index] = StringToEnum(value)
+	end
 
 	# compute cost function
 	# TODO: loop through all controls with respect to all the components in the cost function
diff --git a/src/core/modules.jl b/src/core/modules.jl
index 7f3b3f8..c7b9616 100644
--- a/src/core/modules.jl
+++ b/src/core/modules.jl
@@ -277,8 +277,7 @@ function InputUpdateFromSolutionx(analysis::Analysis,ug::IssmVector,femmodel::Fe
 		InputUpdateFromSolution(analysis,ug.vector,femmodel.elements[i])
 	end
 
-	return ug
-
+	return Nothing
 end#}}}
 function InputUpdateFromVectorx(femmodel::FemModel, vector::Vector{Float64}, enum::IssmEnum, layout::IssmEnum)# {{{
 
diff --git a/test/testoptimization.jl b/test/testoptimization.jl
index 25b35f9..55c9c6f 100755
--- a/test/testoptimization.jl
+++ b/test/testoptimization.jl
@@ -29,14 +29,15 @@ md.inversion.dependent_string = ["SurfaceAbsVelMisfit"]
 femmodel=DJUICE.ModelProcessor(md, :StressbalanceSolution)
 n = length(α)
 
+DJUICE.costfunction(α, femmodel)
 # 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))
+autodiff(set_runtime_activity(Enzyme.Reverse), DJUICE.costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
 
 # use user defined grad, errors!
-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,  Optimization.LBFGS())
+#prob = Optimization.OptimizationProblem(optprob, α, femmodel)
+#sol = Optimization.solve(prob,  Optimization.LBFGS())
 #sol = Optimization.solve(prob, Optim.GradientDescent())
 #sol = Optimization.solve(prob, Optim.NelderMead())