Add tests for levenberg least squares

Author: avik-pal

test/nonlinear_least_squares.jl

@@ -0,0 +1,43 @@
+using NonlinearSolve, LinearSolve, LinearAlgebra, Test, Random
+
+true_function(x, θ) = @. θ[1] * exp(θ[2] * x) * cos(θ[3] * x + θ[4])
+true_function(y, x, θ) = (@. y = 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 abs2.(ŷ .- y_target)
+end
+
+function loss_function(resid, θ, p)
+    true_function(resid, p, θ)
+    resid .= abs2.(resid .- y_target)
+    return resid
+end
+
+θ_init = θ_true .+ randn!(similar(θ_true)) * 0.1
+prob_oop = NonlinearLeastSquaresProblem{false}(loss_function, θ_init, x)
+prob_iip = NonlinearLeastSquaresProblem(NonlinearFunction(loss_function;
+        resid_prototype = zero(y_target)), θ_init, x)
+
+# sol = solve(prob_oop, GaussNewton(); maxiters = 1000, abstol = 1e-8)
+# @test SciMLBase.successful_retcode(sol)
+# @test norm(sol.resid) < 1e-6
+
+# sol = solve(prob_iip, GaussNewton(); maxiters = 1000, abstol = 1e-8)
+# @test SciMLBase.successful_retcode(sol)
+# @test norm(sol.resid) < 1e-6
+
+sol = solve(prob_oop, LevenbergMarquardt(); maxiters = 1000, abstol = 1e-8)
+@test SciMLBase.successful_retcode(sol)
+@test norm(sol.resid) < 1e-6
+
+sol = solve(prob_iip, LevenbergMarquardt(; linsolve = NormalCholeskyFactorization());
+    maxiters = 1000, abstol = 1e-8)
+@test SciMLBase.successful_retcode(sol)
+@test norm(sol.resid) < 1e-6

test/runtests.jl

@@ -15,6 +15,8 @@ end
     if GROUP == "All" || GROUP == "Core"
         @time @safetestset "Basic Tests + Some AD" include("basictests.jl")
         @time @safetestset "Sparsity Tests" include("sparse.jl")
+
+        @time @safetestset "Nonlinear Least Squares" include("nonlinear_least_squares.jl")
     end
 
     if GROUP == "All" || GROUP == "23TestProblems"