fix: documentation

Author: avik-pal

.github/workflows/CI_NonlinearSolve.yml

@@ -85,7 +85,7 @@ jobs:
           directories: src,ext,lib/SciMLJacobianOperators/src,lib/BracketingNonlinearSolve/src,lib/NonlinearSolveBase/src,lib/NonlinearSolveBase/ext,lib/SimpleNonlinearSolve/src,lib/NonlinearSolveFirstOrder/src,lib/NonlinearSolveSpectralMethods/src,lib/NonlinearSolveQuasiNewton/src
       - uses: codecov/codecov-action@v5
         with:
-          file: lcov.info
+          files: lcov.info
           token: ${{ secrets.CODECOV_TOKEN }}
           verbose: true
           fail_ci_if_error: false

.github/workflows/Documentation.yml

@@ -4,39 +4,27 @@ on:
   push:
     branches:
       - master
-      - 'release-'
-    tags: '*'
+      - "release-"
+    tags: ["*"]
   pull_request:
 
 jobs:
   build:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@latest
+      - uses: julia-actions/setup-julia@v2
         with:
-          version: '1.10'
-      - name: Install dependencies
-        run: |
-          import Pkg
-          Pkg.Registry.update()
-          # Install packages present in subdirectories
-          dev_pks = Pkg.PackageSpec[]
-          for path in ("lib/SciMLJacobianOperators", ".", "lib/SimpleNonlinearSolve", "lib/NonlinearSolveBase", "lib/BracketingNonlinearSolve", "lib/NonlinearSolveFirstOrder", "lib/NonlinearSolveQuasiNewton", "lib/NonlinearSolveSpectralMethods", "lib/NonlinearSolveHomotopyContinuation")
-              push!(dev_pks, Pkg.PackageSpec(; path))
-          end
-          Pkg.develop(dev_pks)
-          Pkg.instantiate()
-        shell: julia --color=yes --project=docs/ {0}
-      - name: Build and deploy
+          version: "1"
+      - uses: julia-actions/cache@v2
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-docdeploy@v1
         env:
-          JULIA_DEBUG: "Documenter"
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
-        run: julia --project=docs/ --code-coverage=user --color=yes docs/make.jl
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v5
         with:
-          file: lcov.info
+          files: lcov.info
           token: ${{ secrets.CODECOV_TOKEN }}
           fail_ci_if_error: false

.gitignore

@@ -26,3 +26,4 @@ docs/src/assets/Project.toml
 
 .vscode
 wip
+.CondaPkg

docs/Project.toml

@@ -11,8 +11,8 @@ DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
-LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 LineSearch = "87fe0de2-c867-4266-b59a-2f0a94fc965b"
+LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 NonlinearSolveBase = "be0214bd-f91f-a760-ac4e-3421ce2b2da0"
 NonlinearSolveFirstOrder = "5959db7a-ea39-4486-b5fe-2dd0bf03d60d"
@@ -28,10 +28,23 @@ SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLJacobianOperators = "19f34311-ddf3-4b8b-af20-060888a46c0e"
 SimpleNonlinearSolve = "727e6d20-b764-4bd8-a329-72de5adea6c7"
 SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
+SparseMatrixColorings = "0a514795-09f3-496d-8182-132a7b665d35"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
 Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 
+[sources]
+BracketingNonlinearSolve = {path = "../lib/BracketingNonlinearSolve"}
+NonlinearSolve = {path = ".."}
+NonlinearSolveBase = {path = "../lib/NonlinearSolveBase"}
+NonlinearSolveFirstOrder = {path = "../lib/NonlinearSolveFirstOrder"}
+NonlinearSolveHomotopyContinuation = {path = "../lib/NonlinearSolveHomotopyContinuation"}
+NonlinearSolveQuasiNewton = {path = "../lib/NonlinearSolveQuasiNewton"}
+NonlinearSolveSciPy = {path = "../lib/NonlinearSolveSciPy"}
+NonlinearSolveSpectralMethods = {path = "../lib/NonlinearSolveSpectralMethods"}
+SciMLJacobianOperators = {path = "../lib/SciMLJacobianOperators"}
+SimpleNonlinearSolve = {path = "../lib/SimpleNonlinearSolve"}
+
 [compat]
 ADTypes = "1.9.0"
 AlgebraicMultigrid = "0.5, 0.6, 1"
@@ -45,8 +58,8 @@ DocumenterCitations = "1"
 DocumenterInterLinks = "1.0.0"
 IncompleteLU = "0.2"
 InteractiveUtils = "<0.0.1, 1"
-LinearSolve = "2, 3"
 LineSearch = "0.1"
+LinearSolve = "2, 3"
 NonlinearSolve = "4"
 NonlinearSolveBase = "1"
 NonlinearSolveFirstOrder = "1"

docs/src/tutorials/large_systems.md

@@ -2,15 +2,15 @@
 
 This tutorial is for getting into the extra features of using NonlinearSolve.jl. Solving
 ill-conditioned nonlinear systems requires specializing the linear solver on properties of
-the Jacobian in order to cut down on the ``\mathcal{O}(n^3)`` linear solve and the
-``\mathcal{O}(n^2)`` back-solves. This tutorial is designed to explain the advanced usage of
+the Jacobian in order to cut down on the `\mathcal{O}(n^3)` linear solve and the
+`\mathcal{O}(n^2)` back-solves. This tutorial is designed to explain the advanced usage of
 NonlinearSolve.jl by solving the steady state stiff Brusselator partial differential
 equation (BRUSS) using NonlinearSolve.jl.
 
 ## Definition of the Brusselator Equation
 
 !!! note
-    
+
     Feel free to skip this section: it simply defines the example problem.
 
 The Brusselator PDE is defined as follows:
@@ -123,38 +123,38 @@ However, if you know the sparsity of your problem, then you can pass a different
 type. For example, a `SparseMatrixCSC` will give a sparse matrix. Other sparse matrix types
 include:
 
-  - Bidiagonal
-  - Tridiagonal
-  - SymTridiagonal
-  - BandedMatrix ([BandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BandedMatrices.jl))
-  - BlockBandedMatrix ([BlockBandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BlockBandedMatrices.jl))
+- Bidiagonal
+- Tridiagonal
+- SymTridiagonal
+- BandedMatrix ([BandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BandedMatrices.jl))
+- BlockBandedMatrix ([BlockBandedMatrices.jl](https://github.com/JuliaLinearAlgebra/BlockBandedMatrices.jl))
 
 ## Approximate Sparsity Detection & Sparse Jacobians
 
 In the next section, we will show how to specify `sparsity` to trigger automatic sparsity
 detection.
 
 ```@example ill_conditioned_nlprob
-import BenchmarkTools # for @btime
+import BenchmarkTools: @btime # for @btime
 
-BenchmarkTools.BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
+@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
 nothing # hide
 ```
 
 ```@example ill_conditioned_nlprob
-import SparseConnectivityTracer
+import SparseConnectivityTracer, SparseMatrixColorings
 
 prob_brusselator_2d_autosparse = NLS.NonlinearProblem(
     NLS.NonlinearFunction(brusselator_2d_loop; sparsity = SparseConnectivityTracer.TracerSparsityDetector()),
     u0, p; abstol = 1e-10, reltol = 1e-10
 )
 
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_autosparse,
+@btime NLS.solve(prob_brusselator_2d_autosparse,
     NLS.NewtonRaphson(; autodiff = ADTypes.AutoForwardDiff(; chunksize = 12)));
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_autosparse,
+@btime NLS.solve(prob_brusselator_2d_autosparse,
     NLS.NewtonRaphson(; autodiff = ADTypes.AutoForwardDiff(; chunksize = 12),
         linsolve = LS.KLUFactorization()));
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_autosparse,
+@btime NLS.solve(prob_brusselator_2d_autosparse,
     NLS.NewtonRaphson(; autodiff = ADTypes.AutoForwardDiff(; chunksize = 12),
         linsolve = LS.KrylovJL_GMRES()));
 nothing # hide
@@ -176,7 +176,7 @@ arguments, and it will kick out a sparse matrix with our pattern, that we can tu
 `jac_prototype`.
 
 !!! tip
-    
+
     External packages like `SparseConnectivityTracer.jl` and `Symbolics.jl` provide the
     actual implementation of sparsity detection.
 
@@ -205,9 +205,9 @@ prob_brusselator_2d_sparse = NLS.NonlinearProblem(ff, u0, p; abstol = 1e-10, rel
 Now let's see how the version with sparsity compares to the version without:
 
 ```@example ill_conditioned_nlprob
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson());
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
+@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson());
+@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson());
+@btime NLS.solve(prob_brusselator_2d_sparse, NLS.NewtonRaphson(linsolve = LS.KLUFactorization()));
 nothing # hide
 ```
 
@@ -223,7 +223,7 @@ Krylov method. To swap the linear solver out, we use the `linsolve` command and
 GMRES linear solver.
 
 ```@example ill_conditioned_nlprob
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
+@btime NLS.solve(prob_brusselator_2d, NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES()));
 nothing # hide
 ```
 
@@ -234,7 +234,7 @@ choices, see the
 `linsolve` choices are any valid [LinearSolve.jl](https://linearsolve.sciml.ai/dev/) solver.
 
 !!! note
-    
+
     Switching to a Krylov linear solver will automatically change the nonlinear problem
     solver into Jacobian-free mode, dramatically reducing the memory required. This can be
     overridden by adding `concrete_jac=true` to the algorithm.
@@ -254,7 +254,7 @@ import IncompleteLU
 
 incompletelu(W, p = nothing) = IncompleteLU.ilu(W, τ = 50.0), LinearAlgebra.I
 
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse,
+@btime NLS.solve(prob_brusselator_2d_sparse,
     NLS.NewtonRaphson(linsolve = LS.KrylovJL_GMRES(precs = incompletelu), concrete_jac = true)
 );
 nothing # hide
@@ -284,7 +284,7 @@ function algebraicmultigrid(W, p = nothing)
     LinearAlgebra.I
 end
 
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_sparse,
+@btime NLS.solve(prob_brusselator_2d_sparse,
     NLS.NewtonRaphson(
         linsolve = LS.KrylovJL_GMRES(; precs = algebraicmultigrid), concrete_jac = true
     )
@@ -304,7 +304,7 @@ function algebraicmultigrid2(W, p = nothing)
     return Pl, LinearAlgebra.I
 end
 
-BenchmarkTools.@btime NLS.solve(
+@btime NLS.solve(
     prob_brusselator_2d_sparse,
     NLS.NewtonRaphson(
         linsolve = LS.KrylovJL_GMRES(precs = algebraicmultigrid2), concrete_jac = true
@@ -331,8 +331,8 @@ prob_brusselator_2d_approx_di = NLS.NonlinearProblem(
         sparsity = DifferentiationInterface.DenseSparsityDetector(ADTypes.AutoForwardDiff(); atol = 1e-4)),
     u0, p; abstol = 1e-10, reltol = 1e-10)
 
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_exact_tracer, NLS.NewtonRaphson());
-BenchmarkTools.@btime NLS.solve(prob_brusselator_2d_approx_di, NLS.NewtonRaphson());
+@btime NLS.solve(prob_brusselator_2d_exact_tracer, NLS.NewtonRaphson());
+@btime NLS.solve(prob_brusselator_2d_approx_di, NLS.NewtonRaphson());
 nothing # hide
 ```
 

lib/SciMLJacobianOperators/src/SciMLJacobianOperators.jl

@@ -419,7 +419,7 @@ function Base.copy(J::StatefulJacobianOperator)
     return StatefulJacobianOperator(
         copy(J.jac_op),
         J.u === nothing ? nothing : copy(J.u),
-        J.p === nothing ? nothing : copy(J.p)
+        applicable(copy, J.p) ? copy(J.p) : J.p
     )
 end