format

docs/src/solvers/NonlinearSystemSolvers.md

@@ -21,13 +21,13 @@ is small, or the eigenvalues of the Jacobian are within a few orders of magnitud
 then `NLSolveJL`'s `:anderson` can be a good choice.
 
 !!! note
-
+    
     `TrustRegion` and `SimpleTrustRegion` are still in development.
 
 ## Full List of Methods
 
 !!! note
-
+    
     For the full details on the capabilities and constructors of the different solvers,
     see the Detailed Solver APIs section!
 
@@ -60,7 +60,7 @@ methods excel at small problems and problems defined with static arrays.
     large-scale nonlinear systems of equations.
 
 !!! note
-
+    
     When used with certain types for the states `u` such as a `Number` or `StaticArray`,
     these solvers are very efficient and non-allocating. These implementations are thus
     well-suited for small systems of equations.

src/NonlinearSolve.jl

@@ -41,24 +41,27 @@ include("ad.jl")
 
 import PrecompileTools
 
-PrecompileTools.@compile_workload begin for T in (Float32, Float64)
-    prob = NonlinearProblem{false}((u, p) -> u .* u .- p, T(0.1), T(2))
+PrecompileTools.@compile_workload begin
+    for T in (Float32, Float64)
+        prob = NonlinearProblem{false}((u, p) -> u .* u .- p, T(0.1), T(2))
 
-    precompile_algs = if VERSION >= v"1.7"
-        (NewtonRaphson(), TrustRegion(), LevenbergMarquardt())
-    else
-        (NewtonRaphson(),)
-    end
+        precompile_algs = if VERSION >= v"1.7"
+            (NewtonRaphson(), TrustRegion(), LevenbergMarquardt())
+        else
+            (NewtonRaphson(),)
+        end
 
-    for alg in precompile_algs
-        solve(prob, alg, abstol = T(1e-2))
-    end
+        for alg in precompile_algs
+            solve(prob, alg, abstol = T(1e-2))
+        end
 
-    prob = NonlinearProblem{true}((du, u, p) -> du[1] = u[1] * u[1] - p[1], T[0.1], T[2])
-    for alg in precompile_algs
-        solve(prob, alg, abstol = T(1e-2))
+        prob = NonlinearProblem{true}((du, u, p) -> du[1] = u[1] * u[1] - p[1], T[0.1],
+                                      T[2])
+        for alg in precompile_algs
+            solve(prob, alg, abstol = T(1e-2))
+        end
     end
-end end
+end
 
 export RadiusUpdateSchemes
 

src/jacobian.jl

@@ -144,5 +144,3 @@ function jacobian_autodiff(f, x::AbstractArray, nonlinfun, alg)
                                 jac_prototype = jac_prototype, chunksize = chunk_size),
      num_of_chunks)
 end
-
-