typos (#398)

.vale.ini

@@ -44,7 +44,7 @@ TokenIgnores = \$(.+)\$,\[.+?\]\(@(ref|id|cite).+?\),`.+`,``.*``,\s{4}.+\n
 BasedOnStyles = Vale, Google
 ; ignore (1) math (2) ref and cite keys (3) code in docs (4) math in docs (5,6) indented blocks
 TokenIgnores = (\$+[^\n$]+\$+)
-Google.We = false # For tutorials we want to adress the user directly.
+Google.We = false # For tutorials we want to address the user directly.
 
 [docs/src/tutorials/*.md]
  ; ignore since they are derived files

Changelog.md

@@ -5,6 +5,12 @@ All notable Changes to the Julia package `Manopt.jl` will be documented in this
 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).
 
+## [0.4.67] – unreleased
+
+### Fixed
+
+* a few typos in the documentation
+
 ## [0.4.66] June 27, 2024
 
 ### Changed

docs/make.jl

@@ -7,7 +7,7 @@ if "--help" ∈ ARGS
         """
 docs/make.jl
 
-Render the `Manopt.jl` documenation with optional arguments
+Render the `Manopt.jl` documentation with optional arguments
 
 Arguments
 * `--exclude-tutorials` - exclude the tutorials from the menu of Documenter,

docs/src/tutorials/GeodesicRegression.md

@@ -154,7 +154,7 @@ t = map(d -> inner(S, m, pca1, log(S, m, d)), data)
      -0.2259012492666664
 
 And we can call the gradient descent. Note that since `gradF!` works in place of `Y`, we have to set the
-`evalutation` type accordingly.
+`evaluation` type accordingly.
 
 ``` julia
 y = gradient_descent(

src/plans/debug.jl

@@ -1075,8 +1075,8 @@ This collected vector is added to the `:Iteration => [...]` pair.
 If necessary, these pairs are created
 
 For each `Pair` of a `Symbol` and a `Vector`, the [`DebugGroupFactory`](@ref)
-is called for the `Vector` and the result is added to the debug dictonaries entry
-with said symbold. This is wrapped into the [`DebugWhenActive`](@ref),
+is called for the `Vector` and the result is added to the debug dictionary's entry
+with said symbol. This is wrapped into the [`DebugWhenActive`](@ref),
 when the `:WhenActive` symbol is present
 
 # Return value
@@ -1160,7 +1160,7 @@ If this results in more than one [`DebugAction`](@ref) a [`DebugGroup`](@ref) of
 If any integers are present, the last of these is used to wrap the group in a
 [`DebugEvery`](@ref)`(k)`.
 
-If `:WhenActive` is present, the resulting Action is wrappedn in [`DebugWhenActive`](@ref),
+If `:WhenActive` is present, the resulting Action is wrapped in [`DebugWhenActive`](@ref),
 making it deactivatable by its parent solver.
 """
 function DebugGroupFactory(a::Vector; activation_offset=1)

src/plans/record.jl

@@ -790,7 +790,7 @@ This collected vector is added to the `:Iteration => [...]` pair.
 If any of these two pairs does not exist, it is pairs are created when adding the corresponding symbols
 
 For each `Pair` of a `Symbol` and a `Vector`, the [`RecordGroupFactory`](@ref)
-is called for the `Vector` and the result is added to the debug dictionaries entry
+is called for the `Vector` and the result is added to the debug dictionary's entry
 with said symbol. This is wrapped into the [`RecordWhenActive`](@ref),
 when the `:WhenActive` symbol is present
 

src/solvers/cma_es.jl

@@ -620,7 +620,7 @@ function status_summary(c::StopWhenBestCostInGenerationConstant)
 end
 function get_reason(c::StopWhenBestCostInGenerationConstant)
     if c.at_iteration >= 0
-        return "At iteration $(c.at_iteration): for the last $(c.iterations_since_change) generatiosn the best objective value in each generation was equal to $(c.best_objective_at_last_change).\n"
+        return "At iteration $(c.at_iteration): for the last $(c.iterations_since_change) generations the best objective value in each generation was equal to $(c.best_objective_at_last_change).\n"
     end
     return ""
 end
@@ -880,7 +880,7 @@ function status_summary(c::StopWhenPopulationCostConcentrated)
 end
 function get_reason(c::StopWhenPopulationCostConcentrated)
     if c.at_iteration >= 0
-        return "Range of best objective function values in the last $(length(c.best_value_history)) gnerations and all values in the current generation is below $(c.tol)\n"
+        return "Range of best objective function values in the last $(length(c.best_value_history)) generations and all values in the current generation is below $(c.tol)\n"
     end
     return ""
 end

src/solvers/difference-of-convex-proximal-point.jl

@@ -431,7 +431,7 @@ function initialize_solver!(::AbstractManoptProblem, dcps::DifferenceOfConvexPro
     return dcps
 end
 #=
-    Varant I: allocating closed form of the prox
+    Variant I: allocating closed form of the prox
 =#
 function step_solver!(
     amp::AbstractManoptProblem,
@@ -450,7 +450,7 @@ function step_solver!(
 end
 
 #=
-    Varant II: in-place closed form of the prox
+    Variant II: in-place closed form of the prox
 =#
 function step_solver!(
     amp::AbstractManoptProblem,
@@ -468,7 +468,7 @@ function step_solver!(
     return dcps
 end
 #=
-    Varant III: subsolver variant of the prox
+    Variant III: subsolver variant of the prox
 =#
 function step_solver!(
     amp::AbstractManoptProblem,

src/solvers/truncated_conjugate_gradient_descent.jl

@@ -744,7 +744,7 @@ function step_solver!(
         tcgs.model_value = new_model_value
         return tcgs
     end
-    # otherweise accept step
+    # otherwise accept step
     copyto!(M, tcgs.Y, p, new_Y)
     tcgs.model_value = new_model_value
     copyto!(M, tcgs.HY, p, new_HY)

test/plans/test_cache.jl

@@ -282,7 +282,7 @@ A `SimpleManifoldCachedObjective`""",
         )
         # undecorated / recursive cost -> exactly f
         @test Manopt.get_cost_function(obj) === Manopt.get_cost_function(c_obj, true)
-        # otherise different
+        # otherwise different
         f1 = Manopt.get_cost_function(c_obj)
         @test f1 != f
         @test f1(M, p) == f(M, p)
@@ -331,7 +331,7 @@ A `SimpleManifoldCachedObjective`""",
         s_obj = Manopt.SimpleManifoldCachedObjective(M, obj_g; p=similar(p), X=similar(X))
         # undecorated / recursive cost -> exactly f
         @test Manopt.get_cost_function(obj_g) === Manopt.get_cost_function(s_obj, true)
-        # otherise different
+        # otherwise different
         f1 = Manopt.get_cost_function(s_obj)
         @test f1 != f
         @test f1(M, p) == f(M, p)

test/plans/test_counts.jl

@@ -71,7 +71,7 @@ include("../utils/dummy_types.jl")
         c_obj = ManifoldCountObjective(M, obj, [:Cost, :Gradient, :Hessian])
         # undecorated / recursive cost -> exactly f
         @test Manopt.get_cost_function(obj) === Manopt.get_cost_function(c_obj, true)
-        # otherise different
+        # otherwise different
         f1 = get_cost_function(c_obj)
         @test f1 != f
         @test f1(M, p) == f(M, p)

test/plans/test_embedded.jl

@@ -91,7 +91,7 @@ using Manifolds, Manopt, Test, LinearAlgebra, Random
         e_obj = EmbeddedManifoldObjective(obj)
         # undecorated / recursive cost -> exactly f
         @test Manopt.get_cost_function(obj) === Manopt.get_cost_function(e_obj, true)
-        # otherise different
+        # otherwise different
         f1 = Manopt.get_cost_function(e_obj)
         @test f1 != f
         @test f1(M, p) == f(M, p)

tutorials/GeodesicRegression.qmd

@@ -188,7 +188,7 @@ t = map(d -> inner(S, m, pca1, log(S, m, d)), data)
 ```
 
 And we can call the gradient descent. Note that since `gradF!` works in place of `Y`, we have to set the
-`evalutation` type accordingly.
+`evaluation` type accordingly.
 
 ```{julia}
 y = gradient_descent(

tutorials/HowToDebug.qmd

@@ -76,7 +76,7 @@ p1 = exact_penalty_method(
 While in the last step, we specified what to print, this can be extend to even specify _when_ to print it. Currently the following four “places” are available, ordered by when they appear
 in an algorithm run.
 
-* `:Start` to print something at the start of the algorith. At this place all other (the following) places are “reset”, by triggering each of them with an iteration number `0`
+* `:Start` to print something at the start of the algorithm. At this place all other (the following) places are “reset”, by triggering each of them with an iteration number `0`
 * `:BeforeIteration` to print something before an iteration starts
 * `:Iteration` to print something _after_ an iteration. For example the group of prints from
 the last code block `[:Iteration, :Cost, " | ", :ϵ, 25,]` is added to this entry.
@@ -101,7 +101,7 @@ p1 = exact_penalty_method(
 );
 ```
 
-This also illustrates, that instead of `Symbol`s we can also always pass down a [`DebugAction`](@ref) directly, for example when there is a reason to create or configure the action more individually thatn the default from the symbol.
+This also illustrates, that instead of `Symbol`s we can also always pass down a [`DebugAction`](@ref) directly, for example when there is a reason to create or configure the action more individually than the default from the symbol.
 Note that the number (`25`) yields that all but `:Start` and `:Stop` are only displayed every twenty-fifth iteration.
 
 ## Subsolver debug

tutorials/ImplementOwnManifold.qmd

@@ -47,7 +47,7 @@ Random.seed!(42)
 #| echo: false
 #| code-fold: true
 #| output: false
-# to keep the output and usage simple let's dactivate tutorial mode here
+# to keep the output and usage simple let's deactivate tutorial mode here
 Manopt.set_manopt_parameter!(:Mode, "None")
 ```
 
@@ -183,7 +183,7 @@ Let's discuss these in the next steps.
 q1 = gradient_descent(M, f, grad_f, p0;
     retraction_method = ProjectionRetraction(),   # state, that we use the retraction from above
     stepsize = DecreasingStepsize(M; length=1.0), # A simple step size
-    stopping_criterion = StopAfterIteration(10),  # A simple stopping crtierion
+    stopping_criterion = StopAfterIteration(10),  # A simple stopping criterion
     X = zeros(d+1),                               # how we define/represent tangent vectors
 )
 f(M,q1)