chore: formatting

.github/workflows/FormatCheck.yml

@@ -7,3 +7,5 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: julia-actions/julia-format@v4
+        with:
+          version: '1'

docs/src/basics/diagnostics_api.md

@@ -70,7 +70,8 @@ cache.timer
 Let's try for some other solver:
 
 ```@example diagnostics_example
-cache = NLS.init(prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50));
+cache = NLS.init(
+    prob, NLS.DFSane(); show_trace = Val(true), trace_level = NLS.TraceMinimal(50));
 NLS.solve!(cache)
 cache.timer
 ```

docs/src/tutorials/large_systems.md

@@ -145,7 +145,8 @@ nothing # hide
 import SparseConnectivityTracer
 
 prob_brusselator_2d_autosparse = NLS.NonlinearProblem(
-    NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
+    NLS.NonlinearFunction(
+        brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
     u0, p; abstol = 1e-10, reltol = 1e-10
 )
 
@@ -186,7 +187,8 @@ import ADTypes
 
 f! = (du, u) -> brusselator_2d_loop(du, u, p)
 du0 = similar(u0)
-jac_sparsity = ADTypes.jacobian_sparsity(f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector())
+jac_sparsity = ADTypes.jacobian_sparsity(
+    f!, du0, u0, SparseConnectivityTracer.TracerSparsityDetector())
 ```
 
 Notice that Julia gives a nice print out of the sparsity pattern. That's neat, and would be
@@ -207,7 +209,8 @@ Now let's see how the version with sparsity compares to the version without:
 ```@example ill_conditioned_nlprob
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson());
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
+BenchmarkTools.@btime NLS.solve(
+    prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
 nothing # hide
 ```
 
@@ -223,7 +226,8 @@ Krylov method. To swap the linear solver out, we use the `linsolve` command and
 GMRES linear solver.
 
 ```@example ill_conditioned_nlprob
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
+BenchmarkTools.@btime NLS.solve(
+    prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
 nothing # hide
 ```
 
@@ -255,7 +259,8 @@ import IncompleteLU
 incompletelu(W, p = nothing) = IncompleteLU.ilu(W, τ = 50.0), LinearAlgebra.I
 
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse,
-    NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true)
+    NLS.NewtonRaphson(
+        linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true)
 );
 nothing # hide
 ```
@@ -280,7 +285,8 @@ which is more automatic. The setup is very similar to before:
 import AlgebraicMultigrid
 
 function algebraicmultigrid(W, p = nothing)
-    return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(AbstractMatrix, W))),
+    return AlgebraicMultigrid.aspreconditioner(AlgebraicMultigrid.ruge_stuben(convert(
+        AbstractMatrix, W))),
     LinearAlgebra.I
 end
 
@@ -324,11 +330,13 @@ import DifferentiationInterface
 import SparseConnectivityTracer
 
 prob_brusselator_2d_exact_tracer = NLS.NonlinearProblem(
-    NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
+    NLS.NonlinearFunction(
+        brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 prob_brusselator_2d_approx_di = NLS.NonlinearProblem(
     NLS.NonlinearFunction(brusselator_2d_loop;
-        sparsity = DifferentiationInterface.DenseSparsityDetector(ADTypes.AutoForwardDiff(); atol = 1e-4)),
+        sparsity = DifferentiationInterface.DenseSparsityDetector(
+            ADTypes.AutoForwardDiff(); atol = 1e-4)),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 
 BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_exact_tracer, NLS.NewtonRaphson());

docs/src/tutorials/nonlinear_solve_gpus.md

@@ -95,7 +95,8 @@ import AMDGPU # For if you have an AMD GPU
 import Metal # For if you have a Mac M-series device and want to use the built-in GPU
 import OneAPI # For if you have an Intel GPU
 
-KernelAbstractions.@kernel function parallel_nonlinearsolve_kernel!(result, @Const(prob), @Const(alg))
+KernelAbstractions.@kernel function parallel_nonlinearsolve_kernel!(
+        result, @Const(prob), @Const(alg))
     i = @index(Global)
     prob_i = SciMLBase.remake(prob; p = prob.p[i])
     sol = NLS.solve(prob_i, alg)

docs/src/tutorials/optimizing_parameterized_ode.md

@@ -47,7 +47,8 @@ end
 
 p_init = zeros(4)
 
-nlls_prob = NLS.NonlinearLeastSquaresProblem(loss_function, p_init, vec(reduce(hcat, sol.u)))
+nlls_prob = NLS.NonlinearLeastSquaresProblem(
+    loss_function, p_init, vec(reduce(hcat, sol.u)))
 ```
 
 Now, we can use any NLLS solver to solve this problem.

ext/NonlinearSolveNLSolversExt.jl

@@ -34,7 +34,7 @@ function SciMLBase.__solve(
         )
 
         fj_scalar = @closure (Jx,
-            x) -> begin
+        x) -> begin
             return DifferentiationInterface.value_and_derivative(
                 prob.f, prep, autodiff, x, Constant(prob.p)
             )

ext/NonlinearSolvePETScExt.jl

@@ -50,8 +50,8 @@ function SciMLBase.__solve(
     nf = Ref{Int}(0)
 
     f! = @closure (cfx,
-        cx,
-        user_ctx) -> begin
+    cx,
+    user_ctx) -> begin
         nf[] += 1
         fx = cfx isa Ptr{Nothing} ? PETSc.unsafe_localarray(T, cfx; read = false) : cfx
         x = cx isa Ptr{Nothing} ? PETSc.unsafe_localarray(T, cx; write = false) : cx
@@ -90,9 +90,9 @@ function SciMLBase.__solve(
         if J_init isa AbstractSparseMatrix
             PJ = PETSc.MatSeqAIJ(J_init)
             jac_fn! = @closure (cx,
-                J,
-                _,
-                user_ctx) -> begin
+            J,
+            _,
+            user_ctx) -> begin
                 njac[] += 1
                 x = cx isa Ptr{Nothing} ? PETSc.unsafe_localarray(T, cx; write = false) : cx
                 if J isa PETSc.AbstractMat
@@ -110,9 +110,9 @@ function SciMLBase.__solve(
         else
             PJ = PETSc.MatSeqDense(J_init)
             jac_fn! = @closure (cx,
-                J,
-                _,
-                user_ctx) -> begin
+            J,
+            _,
+            user_ctx) -> begin
                 njac[] += 1
                 x = cx isa Ptr{Nothing} ? PETSc.unsafe_localarray(T, cx; write = false) : cx
                 jac!(J, x)

lib/BracketingNonlinearSolve/ext/BracketingNonlinearSolveForwardDiffExt.jl

@@ -8,8 +8,8 @@ using SciMLBase: SciMLBase, IntervalNonlinearProblem
 using BracketingNonlinearSolve: Bisection, Brent, Alefeld, Falsi, ITP, Ridder
 
 const DualIntervalNonlinearProblem{T,
-    V,
-    P} = IntervalNonlinearProblem{
+V,
+P} = IntervalNonlinearProblem{
     uType, iip, <:Union{<:Dual{T, V, P}, <:AbstractArray{<:Dual{T, V, P}}}
 } where {uType, iip}
 

lib/BracketingNonlinearSolve/test/rootfind_tests.jl

@@ -18,7 +18,7 @@ end
 
         @testset for p in 1.1:0.1:100.0
             @test g(p)≈sqrt(p) atol=1e-3 rtol=1e-3
-            @test ForwardDiff.derivative(g, p)≈1/(2*sqrt(p)) atol=1e-3 rtol=1e-3
+            @test ForwardDiff.derivative(g, p)≈1 / (2 * sqrt(p)) atol=1e-3 rtol=1e-3
         end
 
         t = (p) -> [sqrt(p[2] / p[1])]
@@ -30,7 +30,7 @@ end
             return [sol.u]
         end
 
-        @test g2(p)≈[sqrt(p[2]/p[1])] atol=1e-3 rtol=1e-3
+        @test g2(p)≈[sqrt(p[2] / p[1])] atol=1e-3 rtol=1e-3
         @test ForwardDiff.jacobian(g2, p)≈ForwardDiff.jacobian(t, p) atol=1e-3 rtol=1e-3
 
         probB = IntervalNonlinearProblem{false}(quadratic_f, (1.0, 2.0), 2.0)
@@ -50,8 +50,8 @@ end
 end
 
 @testitem "Tolerance Tests Interval Methods" setup=[RootfindingTestSnippet] tags=[:core] begin
-    prob=IntervalNonlinearProblem(quadratic_f, (1.0, 20.0), 2.0)
-    ϵ=eps(Float64) # least possible tol for all methods
+    prob = IntervalNonlinearProblem(quadratic_f, (1.0, 20.0), 2.0)
+    ϵ = eps(Float64) # least possible tol for all methods
 
     @testset for alg in (Bisection(), Falsi(), ITP(), Muller(), nothing)
         @testset for abstol in [0.1, 0.01, 0.001, 0.0001, 1e-5, 1e-6]

lib/NonlinearSolveBase/src/NonlinearSolveBase.jl

@@ -71,7 +71,7 @@ include("forward_diff.jl")
 @compat(public, (construct_jacobian_cache,))
 @compat(public,
     (assert_extension_supported_termination_condition,
-    construct_extension_function_wrapper, construct_extension_jac))
+        construct_extension_function_wrapper, construct_extension_jac))
 
 export TraceMinimal, TraceWithJacobianConditionNumber, TraceAll
 

lib/NonlinearSolveBase/src/autodiff.jl

@@ -119,7 +119,7 @@ function nlls_generate_vjp_function(prob::NonlinearLeastSquaresProblem, sol, uu)
     if SciMLBase.has_vjp(prob.f)
         if SciMLBase.isinplace(prob)
             return @closure (
-                du, u, p) -> begin
+            du, u, p) -> begin
                 resid = Utils.safe_similar(du, length(sol.resid))
                 prob.f(resid, u, p)
                 prob.f.vjp(du, resid, u, p)
@@ -128,15 +128,15 @@ function nlls_generate_vjp_function(prob::NonlinearLeastSquaresProblem, sol, uu)
             end
         else
             return @closure (
-                u, p) -> begin
+            u, p) -> begin
                 resid = prob.f(u, p)
                 return reshape(2 .* prob.f.vjp(resid, u, p), size(u))
             end
         end
     elseif SciMLBase.has_jac(prob.f)
         if SciMLBase.isinplace(prob)
             return @closure (
-                du, u, p) -> begin
+            du, u, p) -> begin
                 J = Utils.safe_similar(du, length(sol.resid), length(u))
                 prob.f.jac(J, u, p)
                 resid = Utils.safe_similar(du, length(sol.resid))
@@ -146,7 +146,7 @@ function nlls_generate_vjp_function(prob::NonlinearLeastSquaresProblem, sol, uu)
             end
         else
             return @closure (u,
-                p) -> begin
+            p) -> begin
                 return reshape(2 .* vec(prob.f(u, p))' * prob.f.jac(u, p), size(u))
             end
         end
@@ -157,7 +157,7 @@ function nlls_generate_vjp_function(prob::NonlinearLeastSquaresProblem, sol, uu)
 
         if SciMLBase.isinplace(prob)
             return @closure (
-                du, u, p) -> begin
+            du, u, p) -> begin
                 resid = Utils.safe_similar(du, length(sol.resid))
                 prob.f(resid, u, p)
                 # Using `Constant` lead to dual ordering issues
@@ -169,7 +169,7 @@ function nlls_generate_vjp_function(prob::NonlinearLeastSquaresProblem, sol, uu)
             end
         else
             return @closure (u,
-                p) -> begin
+            p) -> begin
                 v = prob.f(u, p)
                 # Using `Constant` lead to dual ordering issues
                 res = only(DI.pullback(Base.Fix2(prob.f, p), autodiff, u, (v,)))

lib/NonlinearSolveBase/src/public.jl

@@ -87,7 +87,6 @@ for name in (:Norm, :RelNorm, :AbsNorm)
 end
 
 for norm_type in (:RelNorm, :AbsNorm), safety in (:Safe, :SafeBest)
-
     struct_name = Symbol(norm_type, safety, :TerminationMode)
     supertype_name = Symbol(:Abstract, safety, :NonlinearTerminationMode)
 

lib/NonlinearSolveFirstOrder/src/NonlinearSolveFirstOrder.jl

@@ -56,7 +56,7 @@ include("forward_diff.jl")
         (NonlinearFunction{false, NoSpecialize}((u, p) -> (u .^ 2 .- p)[1:1]), [0.1, 0.0]),
         (
             NonlinearFunction{false, NoSpecialize}((
-                u, p) -> vcat(u .* u .- p, u .* u .- p)),
+            u, p) -> vcat(u .* u .- p, u .* u .- p)),
             [0.1, 0.1]
         ),
         (
@@ -84,12 +84,10 @@ include("forward_diff.jl")
     @compile_workload begin
         @sync begin
             for prob in nonlinear_problems, alg in nlp_algs
-
                 Threads.@spawn CommonSolve.solve(prob, alg; abstol = 1e-2, verbose = false)
             end
 
             for prob in nlls_problems, alg in nlls_algs
-
                 Threads.@spawn CommonSolve.solve(prob, alg; abstol = 1e-2, verbose = false)
             end
         end

lib/NonlinearSolveFirstOrder/src/solve.jl

@@ -125,7 +125,8 @@ function SciMLBase.__init(
         stats = NLStats(0, 0, 0, 0, 0), alias_u0 = false, maxiters = 1000,
         abstol = nothing, reltol = nothing, maxtime = nothing,
         termination_condition = nothing, internalnorm::IN = L2_NORM,
-        linsolve_kwargs = (;), initializealg = NonlinearSolveBase.NonlinearSolveDefaultInit(), kwargs...
+        linsolve_kwargs = (;), initializealg = NonlinearSolveBase.NonlinearSolveDefaultInit(),
+        kwargs...
 ) where {IN}
     @set! alg.autodiff = NonlinearSolveBase.select_jacobian_autodiff(prob, alg.autodiff)
     provided_jvp_autodiff = alg.jvp_autodiff !== nothing

lib/NonlinearSolveFirstOrder/test/least_squares_tests.jl

@@ -20,7 +20,6 @@ for linsolve in [nothing, LUFactorization(), KrylovJL_GMRES(), KrylovJL_LSMR()]
     vjp_autodiffs = linsolve isa KrylovJL ? [nothing, AutoZygote(), AutoFiniteDiff()] :
                     [nothing]
     for linesearch in linesearches, vjp_autodiff in vjp_autodiffs
-
         push!(solvers, GaussNewton(; linsolve, linesearch, vjp_autodiff))
     end
 end
@@ -47,11 +46,10 @@ end
 @testitem "General NLLS Solvers" setup=[CoreNLLSTesting] tags=[:core] begin
     using LinearAlgebra
 
-    nlls_problems=[prob_oop, prob_iip, prob_oop_vjp, prob_iip_vjp]
+    nlls_problems = [prob_oop, prob_iip, prob_oop_vjp, prob_iip_vjp]
 
     for prob in nlls_problems, solver in solvers
-
-        sol=solve(prob, solver; maxiters = 10000, abstol = 1e-6)
+        sol = solve(prob, solver; maxiters = 10000, abstol = 1e-6)
         @test SciMLBase.successful_retcode(sol)
         @test norm(sol.resid, 2) < 1e-6
     end