Add OptimizationFunction conversion and adtype arg and tests

Author: Vaibhavdixit02

src/problems/basic_problems.jl

@@ -688,11 +688,18 @@ function OptimizationProblem(f, args...; kwargs...)
     OptimizationProblem{true}(OptimizationFunction{true}(f), args...; kwargs...)
 end
 
-function OptimizationProblem(prob::NonlinearLeastSquaresProblem; kwargs...)
+function OptimizationFunction(f::NonlinearFunction, adtype::AbstractADType = NoAD(); kwargs...)
+    if isinplace(f)
+        throw(ArgumentError("Converting NonlinearFunction to OptimizationFunction is not supported with in-place functions yet."))
+    end
+    OptimizationFunction((u, p) -> sum(abs2, f(u, p)), adtype; kwargs...)
+end
+
+function OptimizationProblem(prob::NonlinearLeastSquaresProblem, adtype::AbstractADType = NoAD(); kwargs...)
     if isinplace(prob)
         throw(ArgumentError("Converting NonlinearLeastSquaresProblem to OptimizationProblem is not supported with in-place functions yet."))
     end
-    optf = OptimizationFunction(sum ∘ prob.f, grad = (Jv, u, p) -> prob.f.jvp(Jv, prob.f(u, p), u, p), kwargs...)
+    optf = OptimizationFunction(prob.f, adtype; kwargs...)
     return OptimizationProblem(optf, prob.u0, prob.p; prob.kwargs..., kwargs...)
 end
 

test/downstream/nllsopt.jl

@@ -0,0 +1,29 @@
+using NonlinearSolve, Optimization, OptimizationNLopt, ForwardDiff
+import FastLevenbergMarquardt, LeastSquaresOptim
+
+true_function(x, θ) = @. θ[1] * exp(θ[2] * x) * cos(θ[3] * x + θ[4])
+
+θ_true = [1.0, 0.1, 2.0, 0.5]
+
+x = [-1.0, -0.5, 0.0, 0.5, 1.0]
+
+y_target = true_function(x, θ_true)
+
+function loss_function(θ, p)
+    ŷ = true_function(p, θ)
+    return ŷ .- y_target
+end
+
+θ_init = θ_true .+ randn!(similar(θ_true)) * 0.1
+prob_oop = NonlinearLeastSquaresProblem{false}(loss_function, θ_init, x)
+
+solver = LevenbergMarquardt()
+
+@time sol = solve(prob, solver; maxiters = 10000, abstol = 1e-8)
+
+optf = OptimizationFunction(prob_oop.f, AutoForwardDiff())
+optprob = OptimizationProblem(optf, prob_oop.u0, prob_oop.p)
+@time sol = solve(optprob, NLopt.LD_LBFGS(); maxiters = 10000, abstol = 1e-8)
+
+optprob = OptimizationProblem(prob_oop, AutoForwardDiff())
+@time sol = solve(optprob, NLopt.LD_LBFGS(); maxiters = 10000, abstol = 1e-8)