Changed some unicode symbols, added github actions

Shuvomoy · Shuvomoy · commit 9920c9409ba9 · 2025-11-27T06:57:52.000-06:00
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
@@ -0,0 +1,7 @@
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"
diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
@@ -0,0 +1,34 @@
+name: CI
+on:
+  push:
+    branches:
+      - main
+    tags: ['*']
+  pull_request:
+  workflow_dispatch:
+concurrency:
+  # Skip intermediate builds: always.
+  # Cancel intermediate builds: only if it is a pull request build.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 60
+    permissions: # needed to allow julia-actions/cache to proactively delete old caches that it has created
+      actions: write
+      contents: read
+    strategy:
+      fail-fast: false
+      matrix:
+        version: ['1.12']
+        os: [ubuntu-latest, windows-latest, macOS-latest]
+    steps:
+      - uses: actions/checkout@v6
+      - uses: julia-actions/setup-julia@v2
+        with:
+          version: ${{ matrix.version }}
+      - uses: julia-actions/cache@v2
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1
diff --git a/.github/workflows/CompatHelper.yml b/.github/workflows/CompatHelper.yml
@@ -0,0 +1,16 @@
+name: CompatHelper
+on:
+  schedule:
+    - cron: 0 0 * * *
+  workflow_dispatch:
+jobs:
+  CompatHelper:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Pkg.add("CompatHelper")
+        run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
+      - name: CompatHelper.main()
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia -e 'using CompatHelper; CompatHelper.main()'
diff --git a/.github/workflows/TagBot.yml b/.github/workflows/TagBot.yml
@@ -0,0 +1,31 @@
+name: TagBot
+on:
+  issue_comment:
+    types:
+      - created
+  workflow_dispatch:
+    inputs:
+      lookback:
+        default: "3"
+permissions:
+  actions: read
+  checks: read
+  contents: write
+  deployments: read
+  issues: read
+  discussions: read
+  packages: read
+  pages: read
+  pull-requests: read
+  repository-projects: read
+  security-events: read
+  statuses: read
+jobs:
+  TagBot:
+    if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: JuliaRegistries/TagBot@v1
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          ssh: ${{ secrets.DOCUMENTER_KEY }}
diff --git a/examples/unconstrained_convex_optimization/accelerated_gradient_strongly_convex.jl b/examples/unconstrained_convex_optimization/accelerated_gradient_strongly_convex.jl
@@ -29,7 +29,7 @@ function wc_accelerated_gradient_strongly_convex(mu, L, n; verbose=false)
 
     if verbose
         @info "🐱 Example file: worst-case performance of the accelerated gradient method" 
-        @info "🫑  PEPit guarantee: f(x_n)-f_*  <= $(round(PEPit_tau, digits=6)) (f(x_0) - f(x_*) + mu/2*||x_0 - x_*||^2)"
+        @info "💻  PEPit guarantee: f(x_n)-f_*  <= $(round(PEPit_tau, digits=6)) (f(x_0) - f(x_*) + mu/2*||x_0 - x_*||^2)"
         @info "📝 Theoretical guarantee: f(x_n)-f_*  <= $(round(theoretical_tau, digits=6)) (f(x_0) - f(x_*) + mu/2*||x_0 - x_*||^2)"
     end
 
diff --git a/src/core/pep.jl b/src/core/pep.jl
@@ -96,7 +96,7 @@ function _eval_points_and_function_values!(pep::PEP, F_val::Vector{Float64}, G_v
     eig_val = ev.values
     eig_vec = ev.vectors
     if minimum(eig_val) < 0
-        verbose && println("🫑 PEPit:  Postprocessing: solver's output is not entirely feasible (smallest eigenvalue: $(minimum(eig_val)) < 0). Projecting Gram matrix.")
+        verbose && println("💻 PEPit:  Postprocessing: solver's output is not entirely feasible (smallest eigenvalue: $(minimum(eig_val)) < 0). Projecting Gram matrix.")
         eig_val = max.(eig_val, 0)
     end
 
@@ -202,7 +202,7 @@ function _logdet_dimension_reduction!(model::JuMP.Model, G, objective, wc_value:
         W = inv(Symmetric(Gcorr + eig_regularization * I(pc)))
 
         @objective(model, Min, sum(W[i, j] * G[i, j] for i in 1:pc, j in 1:pc))
-        verbose && println(" 🫑 PEPit:  Calling SDP solver (logdet step $k)")
+        verbose && println(" 💻 PEPit:  Calling SDP solver (logdet step $k)")
         optimize!(model)
 
         wc_value = value(objective)
@@ -211,8 +211,8 @@ function _logdet_dimension_reduction!(model::JuMP.Model, G, objective, wc_value:
         nb2, thr2, Gcorr = _get_nb_eigs_and_corrected(Gval)
 
         if verbose
-            println(" 🫑 PEPit:  Solver status: $(termination_status(model)); objective value: $(wc_value)")
-            println(" 🫑 PEPit:  Postprocessing: $nb2 eigenvalue(s) > $thr2 after $k logdet step(s)")
+            println(" 💻 PEPit:  Solver status: $(termination_status(model)); objective value: $(wc_value)")
+            println(" 💻 PEPit:  Postprocessing: $nb2 eigenvalue(s) > $thr2 after $k logdet step(s)")
         end
     end
 
@@ -222,7 +222,8 @@ end
 
 
 function solve!(pep::PEP;
-    solver=Mosek.Optimizer,
+    solver=Clarabel.Optimizer,
+    # Options for solver: Clarabel.Optimizer, Mosek.Optimizer
     verbose::Bool=true,
     tracetrick::Bool=false,
     logdetiters::Int=0,
@@ -243,7 +244,7 @@ function solve!(pep::PEP;
     end
 
     pc, ec = Point_counter[], Expression_counter[]
-    verbose && println(" 🫑 PEPit:  Setting up the problem: size of the main PSD matrix: $(pc)x$(pc)")
+    verbose && println(" 💻 PEPit:  Setting up the problem: size of the main PSD matrix: $(pc)x$(pc)")
 
     @variable(model, objective)
 
@@ -253,15 +254,15 @@ function solve!(pep::PEP;
     main_psd_ref = @constraint(model, G in PSDCone())
 
 
-    verbose && println(" 🫑 PEPit:  Setting up the problem: performance measure is minimum of $(length(pep.list_of_performance_metrics)) element(s)")
+    verbose && println(" 💻 PEPit:  Setting up the problem: performance measure is minimum of $(length(pep.list_of_performance_metrics)) element(s)")
     perf_con_refs = Vector{Any}()
     for metric in pep.list_of_performance_metrics
         con = @constraint(model, objective <= _expression_to_jump(metric, F, G))
         push!(perf_con_refs, con)
     end
 
 
-    verbose && println(" 🫑 PEPit:  Setting up the problem: Adding initial conditions and general constraints ...")
+    verbose && println(" 💻 PEPit:  Setting up the problem: Adding initial conditions and general constraints ...")
     initial_con_refs = Vector{Any}()
     for cond in pep.list_of_conditions
         expr_jump = _expression_to_jump(cond.expression, F, G)
@@ -270,12 +271,12 @@ function solve!(pep::PEP;
                @constraint(model, expr_jump == 0)
         push!(initial_con_refs, cref)
     end
-    verbose && println(" 🫑 PEPit:  Setting up the problem: initial conditions and general constraints ($(length(pep.list_of_conditions)) constraint(s) added)")
+    verbose && println(" 💻 PEPit:  Setting up the problem: initial conditions and general constraints ($(length(pep.list_of_conditions)) constraint(s) added)")
 
 
     global_psd_refs = Vector{Tuple}()
     if !isempty(pep.list_of_psd)
-        verbose && println(" 🫑 PEPit:  Setting up the problem: $(length(pep.list_of_psd)) lmi constraint(s) added")
+        verbose && println(" 💻 PEPit:  Setting up the problem: $(length(pep.list_of_psd)) lmi constraint(s) added")
         for (k, psd_matrix) in enumerate(pep.list_of_psd)
             n = psd_matrix.shape[1]
             @variable(model, M[1:n, 1:n], Symmetric)
@@ -290,7 +291,7 @@ function solve!(pep::PEP;
     end
 
 
-    verbose && println(" 🫑 PEPit:  Setting up the problem: interpolation conditions for $(length(pep.list_of_functions)) function(s)")
+    verbose && println(" 💻 PEPit:  Setting up the problem: interpolation conditions for $(length(pep.list_of_functions)) function(s)")
     class_con_refs = Vector{Any}()
     class_psd_refs = Vector{Tuple}()
     for (i, f) in enumerate(pep.list_of_functions)
@@ -341,12 +342,12 @@ function solve!(pep::PEP;
     end
 
 
-    verbose && println(" 🫑 PEPit:  Compiling SDP")
+    verbose && println(" 💻 PEPit:  Compiling SDP")
     @objective(model, Max, objective)
-    verbose && println(" 🫑 PEPit:  Calling SDP solver")
+    verbose && println(" 💻 PEPit:  Calling SDP solver")
     optimize!(model)
     if verbose
-        println(" 🫑 PEPit:  Solver status: $(termination_status(model)); optimal value: $(objective |> value)")
+        println(" 💻 PEPit:  Solver status: $(termination_status(model)); optimal value: $(objective |> value)")
     end
     wc_value = value(objective)
 
@@ -355,19 +356,19 @@ function solve!(pep::PEP;
         tol = tol_dimension_reduction
         @constraint(model, objective >= wc_value - tol)
         @objective(model, Min, sum(G[i, i] for i in 1:pc))
-        verbose && println(" 🫑 PEPit:  Calling SDP solver (trace heuristic)")
+        verbose && println(" 💻 PEPit:  Calling SDP solver (trace heuristic)")
         optimize!(model)
         wc_value = value(objective)
         if verbose
-            println(" 🫑 PEPit:  Solver status: $(termination_status(model)); objective value: $(wc_value)")
+            println(" 💻 PEPit:  Solver status: $(termination_status(model)); objective value: $(wc_value)")
         end
     end
 
 
     if logdetiters > 0
         nb, thr, _ = _get_nb_eigs_and_corrected(value.(G))
         if verbose
-            println(" 🫑 PEPit:  Postprocessing: $nb eigenvalue(s) > $thr before dimension reduction")
+            println(" 💻 PEPit:  Postprocessing: $nb eigenvalue(s) > $thr before dimension reduction")
         end
         wc_value = _logdet_dimension_reduction!(model, G, objective, wc_value;
             niter=logdetiters, eig_regularization=eig_regularization,
