diff --git a/src/sparsity_pattern.jl b/src/sparsity_pattern.jl
index 7ab05654..bc3cf3ef 100644
--- a/src/sparsity_pattern.jl
+++ b/src/sparsity_pattern.jl
@@ -51,37 +51,3 @@ function compute_hessian_sparsity(
   S = ADTypes.hessian_sparsity(lagrangian, x0, detector)
   return S
 end
-
-"""
-    dcolors = nnz_colors(trilH, star_set, colors, ncolors)
-
-Determine the coefficients in `trilH` that will be computed by a given color.
-
-Arguments:
-- `trilH::SparseMatrixCSC`: The lower triangular part of a symmetric matrix in CSC format.
-- `star_set::StarSet`: A structure `StarSet` returned by the function `symmetric_coloring_detailed` of SparseMatrixColorings.jl.
-- `colors::Vector{Int}`: A vector where the i-th entry represents the color assigned to the i-th column of the matrix.
-- `ncolors::Int`: The number of distinct colors used in the coloring.
-
-Output:
-- `dcolors::Dict{Int, Vector{Tuple{Int, Int}}}`: A dictionary where the keys are the color indices (from 1 to `ncolors`),
-and the values are vectors of tuples. Each tuple contains two integers: the first integer is the row index, and the
-second integer is the index in `trilH.nzval` where the non-zero coefficient can be found.
-"""
-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)
-
-  n = LinearAlgebra.checksquare(trilH)
-  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))
-    end
-  end
-
-  return dcolors
-end