Fix a few typos.

Author: kellertuer

src/plans/quasi_newton_plan.jl

@@ -754,8 +754,8 @@ taking into account that the corresponding step size is chosen.
 
 # Provided functors
 
-* `(mp::AbstractManoptproblem, st::QuasiNewtonState) -> η` to compute the update direction
-* `(η, mp::AbstractManoptproblem, st::QuasiNewtonState) -> η` to compute the update direction in-place of `η`
+* `(mp::AbstractManoptProblem, st::QuasiNewtonState) -> η` to compute the update direction
+* `(η, mp::AbstractManoptProblem, st::QuasiNewtonState) -> η` to compute the update direction in-place of `η`
 
 # Fields
 

src/plans/stopping_criterion.jl

@@ -244,7 +244,7 @@ any point ``p = (p_1,…,p_n) ∈ $(_math(:M))`` is a vector of length ``n`` wit
 Then, denoting the `outer_norm` by ``r``, the distance of two points ``p,q ∈ $(_math(:M))``
 is given by
 
-```
+```math
 $(_math(:distance))(p,q) = $(_tex(:Bigl))( $(_tex(:sum))_{k=1}^n $(_math(:distance))(p_k,q_k)^r $(_tex(:Bigr)))^{$(_tex(:frac, "1", "r"))},
 ```
 
@@ -575,7 +575,7 @@ any point ``p = (p_1,…,p_n) ∈ $(_math(:M))`` is a vector of length ``n`` wit
 Then, denoting the `outer_norm` by ``r``, the norm of the difference of tangent vectors like the last and current gradien ``X,Y ∈ $(_math(:M))``
 is given by
 
-```
+```math
 $(_tex(:norm, "X-Y"; index = "p")) = $(_tex(:Bigl))( $(_tex(:sum))_{k=1}^n $(_tex(:norm, "X_k-Y_k"; index = "p_k"))^r $(_tex(:Bigr)))^{$(_tex(:frac, "1", "r"))},
 ```
 
@@ -686,8 +686,8 @@ A stopping criterion based on the current gradient norm.
 # Fields
 
 * `norm`:      a function `(M::AbstractManifold, p, X) -> ℝ` that computes a norm
-  of the gradient `X` in the tangent space at `p` on `M``.
-  For manifolds with components provide `(M::AbstractManifold, p, X, r) -> ℝ`.
+  of the gradient `X` in the tangent space at `p` on `M`.
+  For manifolds with components provide a function `(M::AbstractManifold, p, X, r) -> ℝ`.
 * `threshold`: the threshold to indicate to stop when the distance is below this value
 * `outer_norm`: if `M` is a manifold with components, this can be used to specify the norm,
   that is used to compute the overall distance based on the element-wise distance.
@@ -704,7 +704,7 @@ any point ``p = (p_1,…,p_n) ∈ $(_math(:M))`` is a vector of length ``n`` wit
 Then, denoting the `outer_norm` by ``r``, the norm of a tangent vector like the current gradient ``X ∈ $(_math(:M))``
 is given by
 
-```
+```math
 $(_tex(:norm, "X"; index = "p")) = $(_tex(:Bigl))( $(_tex(:sum))_{k=1}^n $(_tex(:norm, "X_k"; index = "p_k"))^r $(_tex(:Bigr)))^{$(_tex(:frac, "1", "r"))},
 ```
 
@@ -1306,10 +1306,12 @@ Note that the cross product is in general noncommutative, and here only the orde
 # Examples
 
 A stopping criterion that indicates to stop whenever the gradient norm is less that `1e-6` for three consecutive iterations:
+
     StopWhenRepeated(StopWhenGradientNormLess(1e-6), 3)
     StopWhenGradientNormLess(1e-6) × 3
 
 A stopping criterion that indicates to stop whenever the gradient norm is less that `1e-6` at three iterations (not necessarily consecutive):
+
     StopWhenRepeated(StopWhenGradientNormLess(1e-6), 3; consecutive=false)
 """
 mutable struct StopWhenRepeated{SC <: StoppingCriterion} <: StoppingCriterion
@@ -1392,7 +1394,9 @@ A stopping criterion, that only evaluates a certain (inner) stopping based on a
 on the iterate `k`.
 The condition is a function `condition(k) -> Bool`.
 
-## Example `(k) -> >(n)` would only activate that stopping criterion after `n` iterations.`.
+## Example
+
+`(k) -> >(n)` would only activate that stopping criterion after `n` iterations.
 
 ## Fields
 
@@ -1409,6 +1413,7 @@ check the inner criterion. The `n` is ignored if you provide a manual functor `c
 ## Examples
 
 A stopping criterion that indicates to stop when the gradient norm is small but only after the third iteration
+
     StopWhenCriterionWithIterationCondition(StopWhenGradientNormLess(1e-6), 3)
 
 You can also use the infix operators `≟` (`\\questeq` on REPL),  `⩻` (`\\ltquest`), and `⩼` (`\\gtquest`) to create such a criterion:

tutorials/.gitignore

@@ -1 +1,3 @@
 /.quarto/
+
+**/*.quarto_ipynb

tutorials/AutomaticDifferentiation.qmd

@@ -141,7 +141,7 @@ More generally take a change of the metric into account as
 = Df(p)[X] = g_p(\operatorname{grad}f(p), X)
 ```
 
-or in words: we have to change the Riesz representer of the (restricted/projected) differential of $f$ ($\tilde f$) to the one with respect to the Riemannian metric. This is done using ``[`change_representer`](@extref `ManifoldsBase.change_representer-Tuple{AbstractManifold, ManifoldsBase.AbstractMetric, Any, Any}`)``{=commonmark}.
+or in words: we have to change the Riesz representer of the (restricted/projected) differential of $f$ ($\tilde f$) to the one with respect to the Riemannian metric. This is done using ``[`change_representer`](@extref `ManifoldsBase.change_representer-Tuple{AbstractManifold, AbstractMetric, Any, Any}`)``{=commonmark}.
 
 ### A continued example
 
@@ -187,7 +187,7 @@ This can be computed for symmetric positive definite matrices by summing the squ
 G(q) = sum(log.(eigvals(Symmetric(q))) .^ 2) / 2
 ```
 
-We can also interpret this as a function on the space of matrices and apply the Euclidean finite differences machinery; in this way we can easily derive the Euclidean gradient. But when computing the Riemannian gradient, we have to change the representer (see again ``[`change_representer`](@extref `ManifoldsBase.change_representer-Tuple{AbstractManifold, ManifoldsBase.AbstractMetric, Any, Any}`)``{=commonmark}) after projecting onto the tangent space $T_p\mathcal P(n)$ at $p$.
+We can also interpret this as a function on the space of matrices and apply the Euclidean finite differences machinery; in this way we can easily derive the Euclidean gradient. But when computing the Riemannian gradient, we have to change the representer (see again ``[`change_representer`](@extref `ManifoldsBase.change_representer-Tuple{AbstractManifold, AbstractMetric, Any, Any}`)``{=commonmark}) after projecting onto the tangent space $T_p\mathcal P(n)$ at $p$.
 
 Let's first define a point and the manifold $N=\mathcal P(3)$.