Update ManifoldDiff.jl compat to 0.4 and CI

.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"

.github/workflows/ci.yml

@@ -11,19 +11,20 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        julia-version: ["1.6", "1.10", "1.11"]
+        julia-version: ["lts", "1", "pre"]
         os: [ubuntu-latest, macOS-latest, windows-latest]
     steps:
       - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.julia-version }}
           arch: x64
       - uses: julia-actions/julia-buildpkg@latest
       - uses: julia-actions/julia-runtest@latest
-      - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v1
+      - uses: julia-actions/julia-processcoverage@latest
+      - uses: codecov/codecov-action@v5
         with:
+          token: ${{ secrets.CODECOV_TOKEN }}
           file: ./lcov.info
           name: codecov-umbrella
           fail_ci_if_error: false

.github/workflows/documenter.yml

@@ -13,15 +13,15 @@ jobs:
       - uses: actions/checkout@v3
       - uses: quarto-dev/quarto-actions/setup@v2
         with:
-          version: "1.3.361"
+          version: "1.6.39"
       - uses: julia-actions/setup-julia@latest
         with:
-          version: "1.10"
+          version: "1.11"
       - name: Julia Cache
-        uses: julia-actions/cache@v1
+        uses: julia-actions/cache@v2
       - name: Cache Quarto
         id: cache-quarto
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           cache-name: cache-quarto
         with:
@@ -31,7 +31,7 @@ jobs:
             ${{ runner.os }}-${{ env.cache-name }}-
       - name: Cache Documenter
         id: cache-documenter
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           cache-name: cache-documenter
         with:
@@ -41,7 +41,7 @@ jobs:
             ${{ runner.os }}-${{ env.cache-name }}-
       - name: Cache CondaPkg
         id: cache-condaPkg
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           cache-name: cache-condapkg
         with:

.github/workflows/format.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: 1
       - name: Install JuliaFormatter and format

Changelog.md

@@ -5,6 +5,14 @@ All notable changes to this Julia package will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.1.11] – 16/12/2024
+
+### Changed
+
+* Added compatibility with ManifoldDiff.jl v0.4
+* Minimum Julia version bumped to 1.10.
+* CI updates
+
 ## [0.1.10] – 18/10/2024
 
 ### Changed

Project.toml

@@ -1,7 +1,7 @@
 name = "ManoptExamples"
 uuid = "5b8d5e80-5788-45cb-83d6-5e8f1484217d"
 authors = ["Ronny Bergmann <[email protected]>"]
-version = "0.1.10"
+version = "0.1.11"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -19,17 +19,17 @@ ManoptExamplesManoptExt = "Manopt"
 
 [compat]
 Distributions = "0.25"
-LinearAlgebra = "1.6"
-ManifoldDiff = "0.3"
-Manifolds = "0.9, 0.10"
+LinearAlgebra = "1.10"
+ManifoldDiff = "0.4"
+Manifolds = "0.10"
 ManifoldsBase = "0.15"
-Manopt = "0.4.7, 0.5"
-Markdown = "1.6"
-Random = "1.6"
-Test = "1.6"
-julia = "1.6"
+Manopt = "0.5"
+Markdown = "1.10"
+Random = "1.10"
+Test = "1.10"
+julia = "1.10"
 RecursiveArrayTools = "2, 3"
-Requires = "0.5, 1"
+Requires = "1"
 
 [extras]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

examples/Bezier-curves.qmd

@@ -126,7 +126,7 @@ end
 ```
 
 Which is the following cure, which clearly starts and ends slower than its speed in the middle,
-which can be seen by the increasing length of the gangent vectors in the middle.
+which can be seen by the increasing length of the tangent vectors in the middle.
 
 ![The original curve](img/bezier/bezier-acceleration-orig.png)
 
@@ -174,7 +174,7 @@ pB_opt = gradient_descent(
         contraction_factor=0.5,
         sufficient_decrease=0.001,
     ),
-    stopping_criterion=StopWhenChangeLess(1e-5) |
+    stopping_criterion=StopWhenChangeLess(N, 1e-5) |
                        StopWhenGradientNormLess(1e-7) |
                        StopAfterIteration(300),
     debug=[

examples/Difference-of-Convex-Frank-Wolfe.qmd

@@ -6,7 +6,7 @@ date: 11/06/2023
 
 ## Introduction
 
-In this example we compare the Difference of Convex Algprithm (DCA) [BergmannFerreiraSantosSouza:2023](@cite)
+In this example we compare the Difference of Convex Algorithm (DCA) [BergmannFerreiraSantosSouza:2023](@cite)
 with the Frank-Wolfe Algorithm, which was introduced in [WeberSra:2022](@cite).
 This example reproduces the results from [BergmannFerreiraSantosSouza:2023](@cite), Section 7.3.
 
@@ -37,7 +37,7 @@ indigo = paul_tol["mutedindigo"]
 teal = paul_tol["mutedteal"]
 ```
 
-We consider the collowing constraint maximimization problem of the Fréchet mean
+We consider the following constraint maximization problem of the Fréchet mean
 on the [symmetric positive definite matrices](https://juliamanifolds.github.io/Manifolds.jl/latest/manifolds/symmetricpositivedefinite.html) $\mathcal P(n)$ with the [affine invariant metric](https://juliamanifolds.github.io/Manifolds.jl/latest/manifolds/symmetricpositivedefinite.html#Default-metric:-the-affine-invariant-metric).
 Let $q_1,\ldots,q_m \in \mathcal P(n)$ be a set of points and $\mu_1,\ldots,\mu_m$ be a set of weights,
 such that they sum to one. We consider then
@@ -131,7 +131,7 @@ and $D=(d_{ij})$.
 @doc raw"""
     closed_form_solution!(M, q, L, U, p X)
 
-Compute the closeed form solution of the constraint sub problem in place of ``q``.
+Compute the closed form solution of the constraint sub problem in place of ``q``.
 """
 function closed_form_solution!(M::SymmetricPositiveDefinite, q, L, U, p, X)
     # extract p^1/2 and p^{-1/2}
@@ -152,7 +152,7 @@ end
 
 ## The Difference of Convex Formulation
 
-We use $g(p) = \iota_{\mathcal C}(p)$ as the indicator funtion of the set $\mathcal C$. We use
+We use $g(p) = \iota_{\mathcal C}(p)$ as the indicator function of the set $\mathcal C$. We use
 
 ```{julia}
 #| output: false
@@ -284,7 +284,7 @@ Let's compare both methods when they have the same stopping criteria
     sub_problem=dc_sub_solution_safe!,
     evaluation=InplaceEvaluation(),
     stopping_criterion = StopAfterIteration(300) |
-        StopWhenChangeLess(1e-14) | StopWhenGradientChangeLess(M, 1e-9),
+        StopWhenChangeLess(M, 1e-14) | StopWhenGradientChangeLess(M, 1e-9),
     debug = [
         (:Iteration, "# %-8d "), (:Cost, "F(p): %0.14f"), (:Change, " |Δp|: %0.14f "),
         (:GradientNorm, " |grad f(p)|: %0.8f "),
@@ -314,7 +314,7 @@ For the statistics we extract the recordings from the state
 
 ## Define the Frank-Wolfe functions
 
-For Frank wolfe, the cost is just defined as $-h(p)$ but the minimisation is constraint to $\mathcal C$, which is enfored by the oracle.
+For Frank wolfe, the cost is just defined as $-h(p)$ but the minimisation is constraint to $\mathcal C$, which is enforced by the oracle.
 
 ```{julia}
 #| output: false
@@ -335,7 +335,7 @@ Similarly we can run the Frank-Wolfe algorithm with
     sub_problem=oracle_fw!,
     evaluation=InplaceEvaluation(),
     stopping_criterion = StopAfterIteration(10^4) |
-        StopWhenChangeLess(1e-14) | StopWhenGradientChangeLess(M, 1e-9),
+        StopWhenChangeLess(M, 1e-14) | StopWhenGradientChangeLess(M, 1e-9),
     debug = [
         (:Iteration, "# %-8d "), :Cost, (:Change, " |Δp|: %0.14f "),
         (:GradientNorm, " |grad f(p)|: %0.8f "),

examples/Difference-of-Convex-Rosenbrock.qmd

@@ -139,7 +139,7 @@ and call the [gradient descent algorithm](https://manoptjl.org/stable/solvers/gr
 Eucl_GD_state = gradient_descent(M, f, ∇f!!, p0;
     evaluation=InplaceEvaluation(),
     debug=[debug_vec...,10^7],
-    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(1e-16),
+    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(M, 1e-16),
     record=[:Iteration, :Cost],
     return_state=true,
 )
@@ -172,7 +172,7 @@ end
 R_GD_state = gradient_descent(M_rb, f, grad_f!, p0;
     evaluation=InplaceEvaluation(),
     debug=[debug_vec...,10^6],
-    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(1e-16),
+    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(M_rb, 1e-16),
     record=[:Iteration, :Cost],
     return_state=true,
 )
@@ -243,7 +243,7 @@ E_doc_state = difference_of_convex_algorithm(
     debug=[debug_vec..., 10^4],
     evaluation=InplaceEvaluation(),
     record=[:Iteration, :Cost],
-    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(1e-16),
+    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(M, 1e-16),
     sub_hess=nothing, # Use gradient descent
     sub_stopping_criterion=StopAfterIteration(2000) | StopWhenGradientNormLess(1e-16),
     return_state=true,
@@ -370,7 +370,7 @@ R_doc_state = difference_of_convex_algorithm(
     debug=[debug_vec..., 10^6],
     evaluation=InplaceEvaluation(),
     record=[:Iteration, :Cost],
-    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(1e-16),
+    stopping_criterion=StopAfterIteration(10^8) | StopWhenChangeLess(M_rb, 1e-16),
     sub_grad=docR_sub_grad,
     sub_hess = nothing, # Use gradient descent
     sub_stopping_criterion=StopAfterIteration(2000) | StopWhenGradientNormLess(1e-16),

examples/H2-Signal-TV.qmd

@@ -275,7 +275,7 @@ sgm_bm_kwargs = [
 ]
 cppa_kwargs = [
     :stopping_criterion => StopWhenAny(
-        StopAfterIteration(max_iters), StopWhenChangeLess(atol)
+        StopAfterIteration(max_iters), StopWhenChangeLess(Hn, atol)
     ),
     :debug => [
         :Iteration,
@@ -294,7 +294,7 @@ cppa_kwargs = [
 ]
 cppa_bm_kwargs = [
     :stopping_criterion => StopWhenAny(
-        StopAfterIteration(max_iters), StopWhenChangeLess(atol)
+        StopAfterIteration(max_iters), StopWhenChangeLess(Hn, atol)
     ),
 ]
 ```

examples/H2_Square_Wave.jl

@@ -240,7 +240,7 @@ c = cyclic_proximal_point(
     proxes,
     p0;
     stopping_criterion=StopWhenAny(
-        StopAfterIteration(max_iters), StopWhenChangeLess(10.0^-8)
+        StopAfterIteration(max_iters), StopWhenChangeLess(Hn, 10.0^-8)
     ),
     debug=[
         :Iteration,
@@ -327,7 +327,7 @@ if benchmarking
         $proxes,
         $p0;
         stopping_criterion=StopWhenAny(
-            StopAfterIteration($max_iters), StopWhenChangeLess(10.0^-8)
+            StopAfterIteration($max_iters), StopWhenChangeLess($Hn, 10.0^-8)
         ),
     )
     #

examples/Project.toml

@@ -22,11 +22,11 @@ Colors = "0.12"
 IJulia = "1"
 LRUCache = "1.4"
 LineSearches = "7"
-ManifoldDiff = "0.3.9"
-Manifolds = "0.9, 0.10"
+ManifoldDiff = "0.4"
+Manifolds = "0.10"
 ManifoldsBase = "0.15"
-Manopt = "0.4.56"
+Manopt = "0.4.56, 0.5"
 NamedColors = "0.2.1"
 Plots = "1.38"
 PythonCall = "0.9"
-julia = "1.6"
+julia = "1.10"

examples/_quarto.yml

@@ -26,4 +26,4 @@ format:
     variant: -raw_html+tex_math_dollars
     wrap: preserve
 
-jupyter: julia-1.10
+jupyter: julia-1.11