Use NaNMath in tests

Author: avik-pal

Project.toml

@@ -65,6 +65,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
+NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 NonlinearProblemLibrary = "b7050fa9-e91f-4b37-bcee-a89a063da141"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -76,4 +77,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt"]
+test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath"]

src/levenberg.jl

@@ -177,7 +177,7 @@ function SciMLBase.__init(prob::Union{NonlinearProblem{uType, iip},
     else
         uf, linsolve, J, fu2, jac_cache, du = jacobian_caches(alg, f, u, p, Val(iip);
             linsolve_kwargs, linsolve_with_JᵀJ)
-        JᵀJ = similar(u)
+        JᵀJ = similar(_vec(u))
         J² = similar(J)
         v = similar(du)
     end
@@ -214,7 +214,7 @@ function SciMLBase.__init(prob::Union{NonlinearProblem{uType, iip},
         # Preserve Types
         mat_tmp = vcat(J, DᵀD)
         fill!(mat_tmp, zero(eltype(u)))
-        rhs_tmp = vcat(fu1, u)
+        rhs_tmp = vcat(_vec(fu1), _vec(u))
         fill!(rhs_tmp, zero(eltype(u)))
         linsolve = __setup_linsolve(mat_tmp, rhs_tmp, u, p, alg)
     end

test/polyalgs.jl

@@ -1,4 +1,4 @@
-using NonlinearSolve, Test
+using NonlinearSolve, Test, NaNMath
 
 f(u, p) = u .* u .- 2
 u0 = [1.0, 1.0]
@@ -38,7 +38,8 @@ sol = solve(prob)
 @test SciMLBase.successful_retcode(sol)
 
 # https://github.com/SciML/NonlinearSolve.jl/issues/187
-ff(u, p) = 0.5 / 1.5 * log.(u ./ (1.0 .- u)) .- 2.0 * u .+ 1.0
+# If we use a General Nonlinear Solver the solution might go out of the domain!
+ff(u, p) = 0.5 / 1.5 * NaNMath.log.(u ./ (1.0 .- u)) .- 2.0 * u .+ 1.0
 
 uspan = (0.02, 0.1)
 prob = IntervalNonlinearProblem(ff, uspan)
@@ -48,5 +49,5 @@ sol = solve(prob)
 u0 = 0.06
 p = 2.0
 prob = NonlinearProblem(ff, u0, p)
-solver = solve(prob)
+sol = solve(prob)
 @test SciMLBase.successful_retcode(sol)