Update the tutorial

docs/src/tutorials/advanced.md

@@ -209,8 +209,7 @@ function incompletelu(W, du, u, p, t, newW, Plprev, Prprev, solverdata)
 end
 
 @btime solve(prob_brusselator_2d_sparse,
-    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = incompletelu,
-        concrete_jac = true));
+    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = incompletelu, concrete_jac = true));
 nothing # hide
 ```
 
@@ -275,28 +274,3 @@ nothing # hide
 
 For more information on the preconditioner interface, see the
 [linear solver documentation](https://docs.sciml.ai/LinearSolve/stable/basics/Preconditioners/).
-
-## Speed up Jacobian computation with sparsity exploitation and matrix coloring
-
-To cut down the of Jacobian building overhead, we can choose to exploit the sparsity pattern and deploy matrix coloring during Jacobian construction. With NonlinearSolve.jl, we can simply use `autodiff=AutoSparseForwardDiff()` to automatically exploit the sparsity pattern of Jacobian matrices:
-
-```@example ill_conditioned_nlprob
-@btime solve(prob_brusselator_2d,
-    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = incompletelu, concrete_jac = true,
-        autodiff = AutoSparseForwardDiff()));
-nothing # hide
-```
-
-To setup matrix coloring for the jacobian sparsity pattern, we can simply get the coloring vector by using [ArrayInterface.jl](https://github.com/JuliaArrays/ArrayInterface.jl) for the sparsity pattern of `jac_prototype`:
-
-```@example ill_conditioned_nlprob
-using ArrayInterface
-colorvec = ArrayInterface.matrix_colors(jac_sparsity)
-ff = NonlinearFunction(brusselator_2d_loop; jac_prototype = float.(jac_sparsity), colorvec)
-prob_brusselator_2d_sparse = NonlinearProblem(ff, u0, p)
-
-@btime solve(prob_brusselator_2d_sparse,
-    NewtonRaphson(linsolve = KrylovJL_GMRES(), precs = incompletelu, concrete_jac = true,
-        autodiff = AutoSparseForwardDiff()));
-nothing # hide
-```