Add spell check GitHub Action (#553)

* add spell check GitHub Action
* fix space
* fix a few typos.
* Fix more typos.
* Add a note to contributing.md

---------

Co-authored-by: Ronny Bergmann <git@ronnybergmann.net>

Author: JoshuaLampert

.github/workflows/spell_check.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request, workflow_dispatch]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v5
+      - name: Check spelling
+        uses: crate-ci/typos@v1.40.0

CONTRIBUTING.md

@@ -17,6 +17,7 @@ The following is a set of guidelines to [`Manopt.jl`](https://juliamanifolds.git
     - [Provide a new example](#Provide-a-new-example)
     - [Code style](#Code-style)
     - [Concerning the documentation](#Concerning-the-documentation)
+    - [Spell checking](#Spell-checking)
 ## I just have a question
 
 The developer can most easily be reached in the Julia Slack channel [#manifolds](https://julialang.slack.com/archives/CP4QF0K5Z).
@@ -83,4 +84,8 @@ Please follow a few internal conventions:
 
 If you implement a new feature, a tutorial how to use it would be appreciated as well. Tutorials are written as [Quarto](https://quarto.org/) documents and stored in the `tutorials/` folder. This is rendered automatically into the documentation page, you just have to add a menu entry within the tutorial sub menu.
 
-If you implement an algorithm with a certain numerical example in mind, it would be great, if this could be added to the [ManoptExamples.jl](https://github.com/JuliaManifolds/ManoptExamples.jl) package as well.
+If you implement an algorithm with a certain numerical example in mind, it would be great, if this could be added to the [ManoptExamples.jl](https://github.com/JuliaManifolds/ManoptExamples.jl) package as well.
+
+### Spell checking
+
+We use [crate-ci/typos](https://github.com/crate-ci/typos) for spell checking, which is run automatically on GitHub Actions, but you can also run it locally using their command line tool.

Changelog.md

@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 * a keyword argument `atol` to the `ConstrainedManifoldObjective` to set a tolerance for constraint satisfaction. (#545)
+* a spell checker following [crate-ci/typos](https://github.com/crate-ci/typos)
 
 ### Fixed
 
@@ -21,7 +22,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Removed `atol` from `DebugFeasibility` and instead use the one newly added `atol` from the `ConstrainedManifoldObjective`. (#546)
 * Move from CompatHelper to dependabot to keep track of dependency updates in Julia packages. (#547)
 * moved the `ManoptTestSuite` module to a sub module `Manopt.Test` within `Manopt.jl`,
-so it can be easier resused by others as well (#550)
+so it can be easier reused by others as well (#550)
 * moved to using a `Project.toml` for tests and an overall `[Workspace]`.
   This also allows finally to run single test files without installing all packages manually, but instead just switching to and instantiating the test environment. (#550)
 * for compatibility, state also `[source]` entries consistently in the sub `Project.toml` files. (#550)
@@ -246,7 +247,7 @@ present; they were changed to `retact_fused!`.
 * A scaling error that appeared only when calling `get_cost_function` on the new `ScaledManifoldObjective`.
 * Documentation issues for quasi-Newton solvers.
 * fixes a scaling error in quasi newton
-* Fixes printing of JuMP models containg Manopt solver.
+* Fixes printing of JuMP models containing Manopt solver.
 
 ## [0.5.12] April 13, 2025
 

_typos.toml

@@ -0,0 +1,12 @@
+[default.extend-words]
+methodes = "methodes" # french
+Serie = "Serie" # french
+sur = "sur" # french
+
+[files]
+extend-exclude = [
+"tutorials/*.html",
+# rendered from tutorials/*.qmd -Z we do not want typos twice
+"docs/src/tutorials/*.md",
+"joss/paper.md",
+]

docs/src/plans/index.md

@@ -41,7 +41,7 @@ The following symbols are used.
 | `:TrustRegionRadius` | [`TrustRegionsState`](@ref) | the trust region radius, equivalent to `:σ` |
 | `:ρ`, `:u` | [`ExactPenaltyCost`](@ref), [`ExactPenaltyGrad`](@ref) | Parameters within the exact penalty objective |
 | `:ρ`, `:μ`, `:λ` | [`AugmentedLagrangianCost`](@ref), [`AugmentedLagrangianGrad`](@ref) | Parameters of the Lagrangian function |
-| `:p`, `:X` | [`LinearizedDCCost`](@ref), [`LinearizedDCGrad`](@ref) | Parameters withing the linearized functional used for the sub problem of the [difference of convex algorithm](@ref solver-difference-of-convex) |
+| `:p`, `:X` | [`LinearizedDCCost`](@ref), [`LinearizedDCGrad`](@ref) | Parameters within the linearized functional used for the sub problem of the [difference of convex algorithm](@ref solver-difference-of-convex) |
 
 Any other lower case name or letter as well as single upper case letters access fields of the corresponding first argument.
 for example `:p` could be used to access the field `s.p` of a state.

docs/src/plans/stepsize.md

@@ -41,7 +41,7 @@ ArmijoInitialGuess
 HagerZhangInitialGuess
 ```
 
-Onw implementations can also (just) be functions.
+Own implementations can also (just) be functions.
 
 Some step sizes use [`max_stepsize`](@ref) function as a rough upper estimate for the trust region size.
 It is by default equal to injectivity radius of the exponential map but in some cases a different value is used.

docs/src/references.bib

@@ -276,7 +276,7 @@ @book{doCarmo:1992
 
 @techreport{deCasteljau:1959,
     AUTHOR      = {de Casteljau, Paul},
-    institution   = {Enveloppe Soleau 40.040, Institute National de la Propriété Industrielle, Paris.},
+    institution   = {Envelope Soleau 40.040, Institute National de la Propriété Industrielle, Paris.},
     TITLE       = {Outillage methodes calcul},
     YEAR        = {1959}
 }
@@ -365,7 +365,7 @@ @article{DoddSharrockNemeth:2024
 @techreport{Dreisigmeyer:2007,
     AUTHOR = {Dreisigmeyer, David W.},
     INSTITUTION = {Optimization Online},
-    TITLE = {Direct Search Alogirthms over Riemannian Manifolds},
+    TITLE = {Direct Search Algorithms over Riemannian Manifolds},
     URL = {https://optimization-online.org/?p=9134},
     YEAR = {2007}
 }
@@ -557,7 +557,7 @@ @article{HoseiniMonjeziNobakhtianPouryayevali:2021
 }
 @phdthesis{Huang:2014,
     AUTHOR    = {Huang, W.},
-    SCHOOL    = {Flordia State University},
+    SCHOOL    = {Florida State University},
     TITLE     = {Optimization algorithms on Riemannian manifolds with applications},
     URL       = {https://www.math.fsu.edu/~whuang2/pdf/Huang_W_Dissertation_2013.pdf},
     YEAR      = {2014}
@@ -807,7 +807,7 @@ @article{SouzaOliveira:2015
     PAGES     = {797--810},
     PUBLISHER = {Springer Science and Business Media LLC},
     VOLUME    = {63},
-    TITLE     = {A proximal point algorithm for DC fuctions on Hadamard manifolds},
+    TITLE     = {A proximal point algorithm for DC functions on Hadamard manifolds},
     YEAR      = {2015},
 }
 % --- T

joss/bibliography.bib

@@ -47,7 +47,7 @@ @booklet{Boumal:2020:1
     TITLE  = {An Introduction to Optimization on Smooth Manifolds}
 }
 
-@inproceedings{GousenbourgerMassartMusolasAbsilJaquesHendrickxMarzouk:2017,
+@inproceedings{GousenbourgerMassartMusolasAbsilJacquesHendrickxMarzouk:2017,
     AUTHOR    = {Gousenbourger, Pierre-Yves and Massart, Estelle and Musolas, Antoni and Absil, P.-A. and Jacques, Laurent and Hendrickx, Julien M and Marzouk, Youssef},
     BOOKTITLE = {ESANN2017},
     PAGES     = {305--310},

joss/paper.md

@@ -25,7 +25,7 @@ Based on a generic optimization framework, together with the interface [`Manifol
 # Statement of Need
 
 In many applications and optimization tasks, non-linear data appears naturally.
-For example, when data on the sphere is measured [@GousenbourgerMassartMusolasAbsilJaquesHendrickxMarzouk:2017], diffusion data can be captured as a signal or even multivariate data of symmetric positive definite matrices [@ValkonenBrediesKnoll2013], and orientations like they appear for electron backscattered diffraction (EBSD) data [@BachmannHielscherSchaeben2011]. Another example are fixed rank matrices, appearing in matrix completion [@Vandereycken:2013:1].
+For example, when data on the sphere is measured [@GousenbourgerMassartMusolasAbsilJacquesHendrickxMarzouk:2017], diffusion data can be captured as a signal or even multivariate data of symmetric positive definite matrices [@ValkonenBrediesKnoll2013], and orientations like they appear for electron backscattered diffraction (EBSD) data [@BachmannHielscherSchaeben2011]. Another example are fixed rank matrices, appearing in matrix completion [@Vandereycken:2013:1].
 Working on these data, for example doing data interpolation and approximation [@BergmannGousenbourger:2018:2], denoising [@LellmannStrekalovskiyKoetterCremers:2013:1; @BergmannFitschenPerschSteidl:2018], inpainting [@BergmannChanHielscherPerschSteidl:2016], or performing matrix completion [@GaoAbsil:2021], can usually be phrased as an optimization problem
 
 $$ \text{Minimize}\quad f(x) \quad \text{where } x\in\mathcal M, $$
@@ -82,7 +82,7 @@ In the current version 0.3.17 of `Manopt.jl` the following algorithms are availa
 Given the [`Sphere`](https://juliamanifolds.github.io/Manifolds.jl/v0.7/manifolds/sphere.html) from `Manifolds.jl` and a set of unit vectors $p_1,...,p_N\in\mathbb R^3$, where $N$ is the number of data points,
 we can compute the generalization of the mean, called the Riemannian Center of Mass [@Karcher:1977:1], defined as the minimizer of the squared distances to the given data – a property that the mean in vector spaces fulfills:
 
-$$ \operatorname*{arg\,min}_{x\in\mathcal M}\quad \displaystyle\sum_{k=1}^Nd_{\mathcal M}(x, p_k)^2, $$
+$$ \operatorname*{arg\,min}_{x\in\mathcal M}\quad \displaystyle\sum_{k=1}^N d_{\mathcal M}(x, p_k)^2, $$
 
 where $d_{\mathcal M}$ denotes the length of a shortest geodesic connecting the points specified by its two arguments;
 this is called the Riemannian distance. For the sphere this [`distance`](https://juliamanifolds.github.io/Manifolds.jl/v0.7/manifolds/sphere.html#ManifoldsBase.distance-Tuple{AbstractSphere,%20Any,%20Any}) is given by the length of the shorter great arc connecting the two points.

src/Manopt.jl

@@ -171,11 +171,11 @@ Rn(args...; kwargs...) = Rn(Val(Rn_default()), args...; kwargs...)
     Rn_default()
 
 Specify a default value to dispatch [`Rn`](@ref) on.
-This default is set to `Manifolds`, indicating, that when this package is loded,
+This default is set to `Manifolds`, indicating, that when this package is loaded,
 it is the preferred package to ask for a vector space space.
 
 The default within `Manopt.jl` is to use the [`DefaultManifold`](@extref ManifoldsBase.DefaultManifold) from `ManifoldsBase.jl`.
-If you load `Manifolds.jl` this switches to using [`Euclidan`](@extref Manifolds.Euclidean).
+If you load `Manifolds.jl` this switches to using [`Euclidean`](@extref Manifolds.Euclidean).
 """
 Rn_default() = :Manifolds
 Rn(::Val{T}, args...; kwargs...) where {T} = DefaultManifold(args...; kwargs...)