Paper draft

.github/workflows/draft-pdf.yml

@@ -21,11 +21,32 @@ jobs:
           journal: joss
           # This should be the path to the paper within your repo.
           paper-path: paper/paper.md
-      - name: Upload
+      - name: Upload pdf artifact
         uses: actions/upload-artifact@v4
         with:
           name: paper
           # This is the output path where Pandoc will write the compiled
           # PDF. Note, this should be the same directory as the input
           # paper.md
           path: paper/paper.pdf
+      - name: Create release
+        if: github.event_name == 'push'
+        uses: rymndhng/release-on-push-action@master
+        id: release
+        with:
+          bump_version_scheme: patch
+          tag_prefix: v
+          release_body: ""
+          use_github_release_notes: true
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Upload PDF to release
+        if: github.event_name == 'push'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: paper/paper.pdf
+          asset_name: joss-draft.pdf
+          tag: ${{ steps.release.outputs.tag_name }}
+          overwrite: true
+          body: ""

.gitignore

@@ -0,0 +1,4 @@
+paper/examples/Manifest.toml
+paper/jats/paper.jats
+paper/jats/jso-packages.pdf
+paper/paper.pdf

paper/examples/Bench-utils.jl

@@ -0,0 +1,43 @@
+module BenchUtils
+
+using ProximalAlgorithms
+using ProximalCore
+using NLPModels
+
+export Counting, reset_counters!, make_adnlp_compatible!
+
+(f::AbstractNLPModel)(x) = obj(f,x)
+
+function ProximalAlgorithms.value_and_gradient(f::AbstractNLPModel, x)
+    return obj(f,x), grad(f, x)
+end
+
+"Wrapper compteur pour f ou g (compte #obj, #∇f, #prox)."
+mutable struct Counting{T}
+    f::T
+    eval_count::Int
+    gradient_count::Int
+    prox_count::Int
+end
+Counting(f::T) where {T} = Counting{T}(f, 0, 0, 0)
+
+# f(x)
+(f::Counting)(x) = (f.eval_count += 1; f.f(x))
+
+# (f, ∇f)
+function ProximalAlgorithms.value_and_gradient(f::Counting, x)
+    f.eval_count += 1
+    f.gradient_count += 1
+    return ProximalAlgorithms.value_and_gradient(f.f, x)
+end
+
+# prox!(y, g, x, γ)
+function ProximalCore.prox!(y, g::Counting, x, γ)
+    g.prox_count += 1
+    return ProximalCore.prox!(y, g.f, x, γ)
+end
+
+"Réinitialise les compteurs d’un Counting."
+reset_counters!(c::Counting) = (c.eval_count = 0; c.gradient_count = 0; c.prox_count = 0; nothing)
+
+end # module