🤖 Format .jl files

Author: amontoison

src/sparse_hessian.jl

@@ -4,7 +4,7 @@ struct SparseADHessian{Tag, GT, S, T} <: ADNLPModels.ADBackend
   colptr::Vector{Int}
   colors::Vector{Int}
   ncolors::Int
-  dcolors::Dict{Int, Vector{Tuple{Int,Int}}}
+  dcolors::Dict{Int, Vector{Tuple{Int, Int}}}
   res::S
   lz::Vector{ForwardDiff.Dual{Tag, T, 1}}
   glz::Vector{ForwardDiff.Dual{Tag, T, 1}}
@@ -92,7 +92,7 @@ struct SparseReverseADHessian{T, S, Tagf, F, Tagψ, P} <: ADNLPModels.ADBackend
   colptr::Vector{Int}
   colors::Vector{Int}
   ncolors::Int
-  dcolors::Dict{Int, Vector{Tuple{Int,Int}}}
+  dcolors::Dict{Int, Vector{Tuple{Int, Int}}}
   res::S
   z::Vector{ForwardDiff.Dual{Tagf, T, 1}}
   gz::Vector{ForwardDiff.Dual{Tagf, T, 1}}

src/sparsity_pattern.jl

@@ -72,11 +72,11 @@ function nnz_colors(trilH, star_set, colors, ncolors)
   # We want to determine the coefficients in `trilH` that will be computed by a given color.
   # Because we exploit the symmetry, we also need to store the row index for a given coefficient
   # in the "compressed column".
-  dcolors = Dict(i => Tuple{Int,Int}[] for i=1:ncolors)
+  dcolors = Dict(i => Tuple{Int, Int}[] for i = 1:ncolors)
 
   n = LinearAlgebra.checksquare(trilH)
-  for j in 1:n
-    for k in trilH.colptr[j]:trilH.colptr[j+1]-1
+  for j = 1:n
+    for k = trilH.colptr[j]:(trilH.colptr[j + 1] - 1)
       i = trilH.rowval[k]
       l, c = symmetric_coefficient(i, j, colors, star_set)
       push!(dcolors[c], (l, k))