docs: fix typos

Changelog.md

@@ -48,11 +48,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-* The `AdaptiveWNGrad` stepsize is now availablbe as a new stepsize functor.
+* The `AdaptiveWNGrad` stepsize is now available as a new stepsize functor.
 
 ### Fixed
 
-* Levenberg-Marquardt now posesses its parameters `initial_residual_values` and
+* Levenberg-Marquardt now possesses its parameters `initial_residual_values` and
   `initial_jacobian_f` also as keyword arguments, such that their default initialisations
   can be adapted, if necessary
 
@@ -84,7 +84,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-* More details on the Count and Cache toturial
+* More details on the Count and Cache tutorial
 
 ### Changed
 
@@ -104,7 +104,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   using LRU Caches as a weak dependency. For now this works with cost and gradient evaluations
 * A `ManifoldCountObjective` as a decorator for objectives to enable counting of calls to for example the cost and the gradient
 * adds a `return_objective` keyword, that switches the return of a solver to a tuple `(o, s)`,
-  where `o` is the (possibly decorated) objective, and `s` os the “classical” solver return (state or point).
+  where `o` is the (possibly decorated) objective, and `s` is the “classical” solver return (state or point).
   This way the counted values can be accessed and the cache can be reused.
 * change solvers on the mid level (form `solver(M, objective, p)`) to also accept decorated objectives
 
@@ -123,7 +123,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-* the sub solver for `trust_regions` is now costumizable, i.e. can be exchanged.
+* the sub solver for `trust_regions` is now customizable, i.e. can be exchanged.
 
 ### Changed
 

docs/src/plans/problem.md

@@ -18,7 +18,7 @@ Usually, such a problem is determined by the manifold or domain of the optimisat
 DefaultManoptProblem
 ```
 
-The exception to these are the primal dual-based solvers ([Chambolle-Pock](@ref ChambollePockSolver) and the [PD Semismooth Newton](@ref PDRSSNSolver)]), which both need two manifolds as their domain(s), hence thre also exists a
+The exception to these are the primal dual-based solvers ([Chambolle-Pock](@ref ChambollePockSolver) and the [PD Semismooth Newton](@ref PDRSSNSolver)]), which both need two manifolds as their domain(s), hence there also exists a
 
 ```@docs
 TwoManifoldProblem

docs/src/references.md

@@ -1,6 +1,6 @@
 # Literature
 
-This is all literature mentioned / referenced in the `Manopt.jl` documenation.
+This is all literature mentioned / referenced in the `Manopt.jl` documentation.
 Usually you will find a small reference section at the end of every documentation page that contains references.
 
 ```@bibliography

docs/src/solvers/index.md

@@ -101,7 +101,7 @@ Then it would call the iterate process.
 
 ### The manual call
 
-If you generate the correctsponding `problem` and `state` as the previous step does, you can
+If you generate the corresponding `problem` and `state` as the previous step does, you can
 also use the third (lowest level) and just call
 
 ```

docs/src/solvers/truncated_conjugate_gradient_descent.md

@@ -109,7 +109,7 @@ is to stop as soon as an iteration ``k`` is reached for which
 
 holds, where ``0 < κ < 1`` and ``θ > 0`` are chosen in advance. This is
 realized in this method by [`StopWhenResidualIsReducedByFactorOrPower`](@ref).
-It can be shown shown that under appropriate conditions the iterates ``x_k``
+It can be shown that under appropriate conditions the iterates ``x_k``
 of the underlying trust-region method converge to nondegenerate critical
 points with an order of convergence of at least ``\min \left( θ + 1, 2 \right)``,
 see [Absil, Mahony, Sepulchre, Princeton University Press, 2008](@cite AbsilMahonySepulchre:2008).

docs/src/tutorials/GeodesicRegression.md

@@ -34,7 +34,7 @@ highlighted = 4;
 ## Time Labeled Data
 
 If for each data item $d_i$ we are also given a time point $t_i\in\mathbb R$, which are pairwise different,
-then we can use the least squares error to state the objetive function as [Fletcher:2013](@cite)
+then we can use the least squares error to state the objective function as [Fletcher:2013](@cite)
 
 ``` math
 F(p,X) = \frac{1}{2}\sum_{i=1}^n d_{\mathcal M}^2(γ_{p,X}(t_i), d_i),
@@ -362,7 +362,7 @@ where $t = (t_1,\ldots,t_n) \in \mathbb R^n$ is now an additional parameter of t
 We write $F_1(p, X)$ to refer to the function on the tangent bundle for fixed values of $t$ (as the one in the last part)
 and $F_2(t)$ for the function $F(p, X, t)$ as a function in $t$ with fixed values $(p, X)$.
 
-For the Euclidean case, there is no neccessity to optimize with respect to $t$, as we saw
+For the Euclidean case, there is no necessity to optimize with respect to $t$, as we saw
 above for the initialization of the fixed time points.
 
 On a Riemannian manifold this can be stated as a problem on the product manifold $\mathcal N = \mathrm{T}\mathcal M \times \mathbb R^n$, i.e.
@@ -380,7 +380,7 @@ N = M × Euclidean(length(t2))
 ```
 
 In this tutorial we present an approach to solve this using an alternating gradient descent scheme.
-To be precise, we define the cost funcion now on the product manifold
+To be precise, we define the cost function now on the product manifold
 
 ``` julia
 struct RegressionCost2{T}
@@ -430,7 +430,7 @@ function (a::RegressionGradient2a!)(N, Y, x)
 end
 ```
 
-Finally, we addionally look for a fixed point $x=(p,X) ∈ \mathrm{T}\mathcal M$ at
+Finally, we additionally look for a fixed point $x=(p,X) ∈ \mathrm{T}\mathcal M$ at
 the gradient with respect to $t∈\mathbb R^n$, i.e. the second component, which is given by
 
 ``` math

docs/src/tutorials/HowToDebug.md

@@ -74,7 +74,7 @@ There is two more advanced variants that can be used. The first is a tuple of a
 
 We can for example change the way the `:ϵ` is printed by adding a format string
 and use [`DebugCost`](@ref)`()` which is equivalent to using `:Cost`.
-Especially with the format change, the lines are more coniststent in length.
+Especially with the format change, the lines are more consistent in length.
 
 ``` julia
 p2 = exact_penalty_method(

docs/src/tutorials/InplaceGradient.md

@@ -1,7 +1,7 @@
 # Speedup using Inplace Evaluation
 Ronny Bergmann
 
-When it comes to time critital operations, a main ingredient in Julia is given by
+When it comes to time critical operations, a main ingredient in Julia is given by
 mutating functions, i.e. those that compute in place without additional memory
 allocations. In the following, we illustrate how to do this with `Manopt.jl`.
 

joss/paper.md

@@ -108,7 +108,7 @@ since its norm is approximately `0.858`. But even projecting this back onto the
 
 In the following figure the data `pts` (teal) and the resulting mean (orange) as well as the projected Euclidean mean (small, cyan) are shown.
 
-![40 random points `pts` and the result from the gradient descent to compute the `x_mean` (orange) compared to a projection of their (Eucliean) mean onto the sphere (cyan).](src/img/MeanIllustr.png)
+![40 random points `pts` and the result from the gradient descent to compute the `x_mean` (orange) compared to a projection of their (Euclidean) mean onto the sphere (cyan).](src/img/MeanIllustr.png)
 
 In order to print the current iteration number, change and cost every iteration as well as the stopping reason, you can provide a `debug` keyword with the corresponding symbols interleaved with strings. The Symbol `:Stop` indicates that the reason for stopping reason should be printed at the end. The last integer in this array specifies that debugging information should be printed only every $i$th iteration.
 While `:x` could be used to also print the current iterate, this usually takes up too much space.