Fix more typos.

Author: kellertuer

Changelog.md

@@ -21,7 +21,7 @@ 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 +246,7 @@ 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.yml

@@ -1,2 +1,9 @@
 [default.extend-words]
-# <typo> = "<correction>"
+# <typo> = "<correction>"
+
+[files]
+extend-exclude = [
+"tutorials/*.html",
+"docs/src/tutorials/*.md", # rendered from tutorials/*.qmd -Z we do not want typos twice
+"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

@@ -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}
 }
@@ -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/documentation_glossary.jl

@@ -11,7 +11,7 @@
 #
 # to keep naming, notation, and formatting
 
-# In general every dictionary here can be either :Symbol-> String or :Symbol -> Dictionary enrties
+# In general every dictionary here can be either :Symbol-> String or :Symbol -> Dictionary entries
 
 _MANOPT_DOC_TYPE = Dict{Symbol, Union{String, Dict, Function}}
 
@@ -130,7 +130,7 @@ _tex(args...; kwargs...) = glossary(:LaTeX, args...; kwargs...)
 define!(:Math, :distance, raw"\mathrm{d}")
 define!(:Math, :M, (; M = "M") -> _math(:Manifold, :symbol; M = M))
 define!(:Math, :Manifold, :symbol, (; M = "M") -> _tex(:Cal, M))
-define!(:Math, :Manifold, :descrption, "the Riemannian manifold")
+define!(:Math, :Manifold, :description, "the Riemannian manifold")
 define!(:Math, :M, (; M = "M") -> _math(:Manifold, :symbol; M = M))
 define!(:Math, :Iterate, (; p = "p", k = "k") -> "$(p)^{($(k))}")
 define!(

src/plans/bundle_plan.jl

@@ -1,5 +1,5 @@
 #
-# Common files for bunlde-based solvers
+# Common files for bundle-based solvers
 #
 
 function convex_bundle_method_subsolver end

src/plans/conjugate_gradient_plan.jl

@@ -164,7 +164,7 @@ function get_gradient(cgs::ConjugateGradientDescentState)
     return cgs.X
 end
 
-_doc_CG_notaion = """
+_doc_CG_notation = """
 Denote the last iterate and gradient by ``p_k,X_k``,
 the current iterate and gradient by ``p_{k+1}, X_{k+1}``, respectively,
 as well as the last update direction by ``δ_k``.
@@ -195,7 +195,7 @@ struct ConjugateDescentCoefficientRule <: DirectionUpdateRule end
 
 Compute the (classical) conjugate gradient coefficient based on [Fletcher:1987](@cite) adapted to manifolds
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 
 Then the coefficient reads
 ```math
@@ -315,7 +315,7 @@ end
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based on [DaiYuan:1999](@cite) adapted to
 Riemannian manifolds.
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 Let ``ν_k = X_{k+1} - $(_math(:vector_transport, :symbol, "p_{k+1}", "p_k"))X_k``,
 where ``$(_math(:vector_transport, :symbol))`` denotes a vector transport.
 
@@ -394,7 +394,7 @@ end
 
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based on [FletcherReeves:1964](@cite) adapted to manifolds
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 
 Then the coefficient reads
 ```math
@@ -493,7 +493,7 @@ end
 
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based on [FletcherReeves:1964](@cite) adapted to manifolds
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 Let ``ν_k = X_{k+1} - $(_math(:vector_transport, :symbol, "p_{k+1}", "p_k"))X_k``,
 where ``$(_math(:vector_transport, :symbol))`` denotes a vector transport.
 
@@ -602,7 +602,7 @@ end
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based on [HestenesStiefel:1952](@cite) adapted to manifolds
 
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 Let ``ν_k = X_{k+1} - $(_math(:vector_transport, :symbol, "p_{k+1}", "p_k"))X_k``,
 where ``$(_math(:vector_transport, :symbol))`` denotes a vector transport.
 
@@ -722,7 +722,7 @@ end
 
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based on [LiuStorey:1991](@cite) adapted to manifolds
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 Let ``ν_k = X_{k+1} - $(_math(:vector_transport, :symbol, "p_{k+1}", "p_k"))X_k``,
 where ``$(_math(:vector_transport, :symbol))`` denotes a vector transport.
 
@@ -819,7 +819,7 @@ end
 Computes an update coefficient for the [`conjugate_gradient_descent`](@ref) algorithm based
 on [PolakRibiere:1969](@cite) adapted to Riemannian manifolds.
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 Let ``ν_k = X_{k+1} - $(_math(:vector_transport, :symbol, "p_{k+1}", "p_k"))X_k``,
 where ``$(_math(:vector_transport, :symbol))`` denotes a vector transport.
 
@@ -1002,7 +1002,7 @@ nearly orthogonal. See [HagerZhang:2006:1; page 12](@cite) (in the preprint, pag
 This method is named after E. Beale from his proceedings paper in 1972 [Beale:1972](@cite).
 This method acts as a _decorator_ to any existing [`DirectionUpdateRule`](@ref) `direction_update`.
 
-$(_doc_CG_notaion)
+$(_doc_CG_notation)
 
 Then a restart is performed, hence ``β_k = 0`` returned if