Format objectives.jl

src/objectives.jl

@@ -20,15 +20,15 @@ struct ScalarObjective{Tf,Tg,Tfg,Tfgh,Th,Thv,Tbf,P}
     param::P
 end
 ScalarObjective(;
-     f = nothing,
-     g = nothing,
-     fg = nothing,
-     fgh = nothing,
-     h = nothing,
-     hv = nothing,
-     batched_f = nothing,
-     param = nothing,
- ) = ScalarObjective(f, g, fg, fgh, h, hv, batched_f, param)
+    f = nothing,
+    g = nothing,
+    fg = nothing,
+    fgh = nothing,
+    h = nothing,
+    hv = nothing,
+    batched_f = nothing,
+    param = nothing,
+) = ScalarObjective(f, g, fg, fgh, h, hv, batched_f, param)
 has_param(so::ScalarObjective) = so.param === nothing ? false : true
 function value(so::ScalarObjective, x)
     if has_param(so)
@@ -118,7 +118,8 @@ struct VectorObjective{TF,TJ,TFJ,TJv}
     FJ::TFJ
     Jv::TJv
 end
-VectorObjective(; F=nothing, J=nothing, FJ=nothing, Jv=nothing) = VectorObjective(F, J, FJ, Jv)
+VectorObjective(; F = nothing, J = nothing, FJ = nothing, Jv = nothing) =
+    VectorObjective(F, J, FJ, Jv)
 
 ## If prob is a NEqProblem, then we can just dispatch to least squares MeritObjective
 # if fast JacVec exists then maybe even line searches that updates the gradient can be used???