test: replace NLsolve with NonlinearSolve (#213)

Author: gdalle

docs/Project.toml

@@ -8,7 +8,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
+NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
@@ -23,5 +23,5 @@ FiniteDiff = "2.29.0"
 ForwardDiff = "1.3.1"
 ImplicitDifferentiation = "0.9.1"
 Literate = "2.21.0"
-NLsolve = "4.5.1"
+NonlinearSolve = "4.17.1"
 Zygote = "0.7.10"

examples/1_basic.jl

@@ -12,7 +12,7 @@ Note that some packages from the [SciML](https://sciml.ai/) ecosystem provide a
 using ForwardDiff
 using ImplicitDifferentiation
 using LinearAlgebra
-using NLsolve
+import NonlinearSolve as NLS
 using Optim
 using Test  #src
 using Zygote
@@ -126,22 +126,23 @@ To make verification easy, we solve the following system:
 ```math
 c(x, y) = y \odot y - x = 0
 ```
-In this case, the optimization problem boils down to the componentwise square root function, but we implement it using a black box solver from [NLsolve.jl](https://github.com/JuliaNLSolvers/NLsolve.jl).
+In this case, the optimization problem boils down to the componentwise square root function, but we implement it using a black box solver from [NonlinearSolve.jl](https://github.com/SciML/NonlinearSolve.jl).
 =#
 
-function forward_nlsolve(x, method)
-    F!(storage, y) = (storage .= y .^ 2 .- x)
+function forward_nlsolve(x)
+    F!(storage, y, x) = (storage .= y .^ 2 .- x)
     initial_y = similar(x)
     initial_y .= 1
-    result = nlsolve(F!, initial_y; method)
-    y = result.zero
+    prob = NLS.NonlinearProblem(F!, initial_y, x; abstol=1e-10, reltol=1e-10)
+    sol = NLS.solve(prob)
+    y = sol.u
     z = nothing
     return y, z
 end;
 
 #-
 
-function conditions_nlsolve(x, y, _z, _method)
+function conditions_nlsolve(x, y, _z)
     c = y .^ 2 .- x
     return c
 end;
@@ -152,27 +153,27 @@ implicit_nlsolve = ImplicitFunction(forward_nlsolve, conditions_nlsolve)
 
 #-
 
-first(implicit_nlsolve(x, :newton)) .^ 2
-@test first(implicit_nlsolve(x, :newton)) .^ 2 ≈ x  #src
+first(implicit_nlsolve(x)) .^ 2
+@test first(implicit_nlsolve(x)) .^ 2 ≈ x  #src
 
 # Forward mode autodiff
 
-ForwardDiff.jacobian(_x -> first(implicit_nlsolve(_x, :newton)), x)
-@test ForwardDiff.jacobian(_x -> first(implicit_nlsolve(_x, :newton)), x) ≈ J  #src
+ForwardDiff.jacobian(_x -> first(implicit_nlsolve(_x)), x)
+@test ForwardDiff.jacobian(_x -> first(implicit_nlsolve(_x)), x) ≈ J  #src
 
 #-
 
-ForwardDiff.jacobian(_x -> first(forward_nlsolve(_x, :newton)), x)
+ForwardDiff.jacobian(_x -> first(forward_nlsolve(_x)), x)
 
 # Reverse mode autodiff
 
-Zygote.jacobian(_x -> first(implicit_nlsolve(_x, :newton)), x)[1]
-@test Zygote.jacobian(_x -> first(implicit_nlsolve(_x, :newton)), x)[1] ≈ J  #src
+Zygote.jacobian(_x -> first(implicit_nlsolve(_x)), x)[1]
+@test Zygote.jacobian(_x -> first(implicit_nlsolve(_x)), x)[1] ≈ J  #src
 
 #-
 
 try
-    Zygote.jacobian(_x -> first(forward_nlsolve(_x, :newton)), x)[1]
+    Zygote.jacobian(_x -> first(forward_nlsolve(_x)), x)[1]
 catch e
     e
 end

test/Project.toml

@@ -13,7 +13,7 @@ ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
+NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
@@ -41,7 +41,7 @@ ImplicitDifferentiation = "0.9.1"
 JET = "0.9, 0.10, 0.11"
 KrylovKit = "0.10.2"
 LinearAlgebra = "1"
-NLsolve = "4.5.1"
+NonlinearSolve = "4.17.1"
 Optim = "1.12.0, 2.0"
 Random = "1"
 SparseArrays = "1"