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SciML · Feb 19, 2023 · 6cdd257 · 6cdd257
1 parent e713d66
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Showing 3 changed files with 6 additions and 5 deletions.
diff --git a/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl b/lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl
@@ -43,7 +43,8 @@ import SnoopPrecompile
 
 SnoopPrecompile.@precompile_all_calls begin for T in (Float32, Float64)
     prob_no_brack = NonlinearProblem{false}((u, p) -> u .* u .- p, T(0.1), T(2))
-    for alg in (SimpleNewtonRaphson, Halley, Broyden, Klement, SimpleTrustRegion, SimpleDFSane)
+    for alg in (SimpleNewtonRaphson, Halley, Broyden, Klement, SimpleTrustRegion,
+                SimpleDFSane)
         solve(prob_no_brack, alg(), abstol = T(1e-2))
     end
 

diff --git a/lib/SimpleNonlinearSolve/src/halley.jl b/lib/SimpleNonlinearSolve/src/halley.jl
@@ -30,7 +30,7 @@ and static array problems.
 """
 struct Halley{CS, AD, FDT} <: AbstractNewtonAlgorithm{CS, AD, FDT}
     function Halley(; chunk_size = Val{0}(), autodiff = Val{true}(),
-                                 diff_type = Val{:forward})
+                    diff_type = Val{:forward})
         new{SciMLBase._unwrap_val(chunk_size), SciMLBase._unwrap_val(autodiff),
             SciMLBase._unwrap_val(diff_type)}()
     end
@@ -73,12 +73,13 @@ function SciMLBase.__solve(prob::NonlinearProblem,
             fx = f(x)
             dfx = FiniteDiff.finite_difference_derivative(f, x, diff_type(alg), eltype(x),
                                                           fx)
-            d2fx = FiniteDiff.finite_difference_derivative(x -> FiniteDiff.finite_difference_derivative(f, x),
+            d2fx = FiniteDiff.finite_difference_derivative(x -> FiniteDiff.finite_difference_derivative(f,
+                                                                                                        x),
                                                            x, diff_type(alg), eltype(x), fx)
         end
         iszero(fx) &&
             return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.Success)
-        Δx = (2*dfx^2 - fx*d2fx) \ (2fx*dfx)
+        Δx = (2 * dfx^2 - fx * d2fx) \ (2fx * dfx)
         x -= Δx
         if isapprox(x, xo, atol = atol, rtol = rtol)
             return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.Success)

diff --git a/lib/SimpleNonlinearSolve/test/basictests.jl b/lib/SimpleNonlinearSolve/test/basictests.jl
@@ -214,7 +214,6 @@ probN = NonlinearProblem(f, u0)
 
 @test solve(probN, Halley()).u ≈ sqrt(2.0)
 
-
 for u0 in [1.0, [1, 1.0]]
     local f, probN, sol
     f = (u, p) -> u .* u .- 2.0