Fix references.

JuliaManifolds · Nov 17, 2023 · 3b03980 · 3b03980
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diff --git a/docs/src/plans/index.md b/docs/src/plans/index.md
@@ -9,7 +9,7 @@ information is required about both the optimisation task or “problem” at han
 This together is called a __plan__ in `Manopt.jl` and it consists of two data structures:
 
 * The [Manopt Problem](@ref ProblemSection) describes all _static_ data of a task, most prominently the manifold and the objective.
-* The [Solver State](@refsec:solver-state) describes all _varying_ data and parameters for the solver that is used. This also means that each solver has its own data structure for the state.
+* The [Solver State](@ref sec-solver-state) describes all _varying_ data and parameters for the solver that is used. This also means that each solver has its own data structure for the state.
 
 By splitting these two parts, one problem can be define an then be solved  using different solvers.
 

diff --git a/docs/src/plans/objective.md b/docs/src/plans/objective.md
@@ -20,7 +20,6 @@ InplaceEvaluation
 evaluation_type
 ```
 
-
 ## Decorators for objectives
 
 An objective can be decorated using the following trait and function to initialize

diff --git a/docs/src/solvers/ChambollePock.md b/docs/src/solvers/ChambollePock.md
@@ -20,7 +20,7 @@ such that ``Λ(\mathcal C) \subset \mathcal D``.
 The algorithm is available in four variants: exact versus linearized (see `variant`)
 as well as with primal versus dual relaxation (see `relax`). For more details, see
 [BergmannHerzogSilvaLouzeiroTenbrinckVidalNunez:2021](@citet*).
-In the following we note the case of the exact, primal relaxed Riemannian Chambolle—Pock algorithm.
+In the following description is the case of the exact, primal relaxed Riemannian Chambolle—Pock algorithm.
 
 Given base points ``m∈\mathcal C``, ``n=Λ(m)∈\mathcal D``,
 initial primal and dual values ``p^{(0)} ∈\mathcal C``, ``ξ_n^{(0)} ∈T_n^*\mathcal N``,
@@ -67,7 +67,7 @@ ChambollePock!
 ChambollePockState
 ```
 
-## Useful Terms
+## Useful terms
 
 ```@docs
 primal_residual

diff --git a/docs/src/solvers/augmented_Lagrangian_method.md b/docs/src/solvers/augmented_Lagrangian_method.md
@@ -15,7 +15,7 @@ CurrentModule = Manopt
 AugmentedLagrangianMethodState
 ```
 
-## Helping Functions
+## Helping functions
 
 ```@docs
 AugmentedLagrangianCost

diff --git a/docs/src/solvers/difference_of_convex.md b/docs/src/solvers/difference_of_convex.md
@@ -18,7 +18,7 @@ difference_of_convex_proximal_point
 difference_of_convex_proximal_point!
 ```
 
-## Manopt Solver States
+## Solver states
 
 ```@docs
 DifferenceOfConvexState

diff --git a/docs/src/solvers/exact_penalty_method.md b/docs/src/solvers/exact_penalty_method.md
@@ -15,7 +15,7 @@ CurrentModule = Manopt
 ExactPenaltyMethodState
 ```
 
-## Helping Functions
+## Helping functions
 
 ```@docs
 ExactPenaltyCost

diff --git a/docs/src/solvers/gradient_descent.md b/docs/src/solvers/gradient_descent.md
@@ -15,7 +15,7 @@ CurrentModule = Manopt
 GradientDescentState
 ```
 
-## Direction Update Rules
+## Direction update rules
 
 A field of the options is the `direction`, a [`DirectionUpdateRule`](@ref), which by default [`IdentityUpdateRule`](@ref) just evaluates the gradient but can be enhanced for example to
 
@@ -27,15 +27,15 @@ AverageGradient
 Nesterov
 ```
 
-## Debug Actions
+## Debug actions
 
 ```@docs
 DebugGradient
 DebugGradientNorm
 DebugStepsize
 ```
 
-## Record Actions
+## Record actions
 
 ```@docs
 RecordGradient
@@ -47,8 +47,7 @@ RecordStepsize
 
 The [`gradient_descent`](@ref) solver requires the following functions of a manifold to be available
 
-* A [retract!](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/retractions/)ion; it is recommended to set the [`default_retraction_method`](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/retractions/#ManifoldsBase.default_retraction_method-Tuple{AbstractManifold}) to a favourite retraction,
-for this case it does not have to be specified.
+* A [retract!](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/retractions/)ion; it is recommended to set the [`default_retraction_method`](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/retractions/#ManifoldsBase.default_retraction_method-Tuple{AbstractManifold}) to a favourite retraction. If this default is set, a `retraction_method=` does not have to be specified.
 * By default gradient descent uses [`ArmijoLinesearch`](@ref) which requires [`max_stepsize`](@ref)`(M)` to be set and an implementation of [`inner`](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/functions/#ManifoldsBase.inner-Tuple%7BAbstractManifold,%20Any,%20Any,%20Any%7D)`(M, p, X)`.
 * By default the stopping criterion uses the [`norm`](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/functions/#LinearAlgebra.norm-Tuple{AbstractManifold,%20Any,%20Any}) as well, to check for a small gradient, but if you implemented `inner` from the last point, the norm is provided already.
 * By default the tangent vector storing the gradient is initialized calling [`zero_vector`](https://juliamanifolds.github.io/ManifoldsBase.jl/stable/functions/#ManifoldsBase.zero_vector-Tuple{AbstractManifold,%20Any})`(M,p)`.

diff --git a/docs/src/solvers/index.md b/docs/src/solvers/index.md
@@ -31,7 +31,7 @@ The following algorithms are currently available
 [Primal-dual Riemannian semismooth Newton Algorithm](@ref PDRSSNSolver) | [`primal_dual_semismooth_Newton`](@ref),  [`PrimalDualSemismoothNewtonState`](@ref) (using [`TwoManifoldProblem`](@ref)) | ``f=F+G(Λ\cdot)``, ``\operatorname{prox}_{σ F}`` & diff., ``\operatorname{prox}_{τ G^*}`` & diff., ``Λ``
 [Quasi-Newton Method](@ref quasiNewton) | [`quasi_Newton`](@ref), [`QuasiNewtonState`](@ref) | ``f``, ``\operatorname{grad} f`` |
 [Steihaug-Toint Truncated Conjugate-Gradient Method](@ref tCG) | [`truncated_conjugate_gradient_descent`](@ref), [`TruncatedConjugateGradientState`](@ref) | ``f``, ``\operatorname{grad} f``, ``\operatorname{Hess} f`` |
-[Subgradient Method](@refsec-subgradient-method) | [`subgradient_method`](@ref), [`SubGradientMethodState`](@ref) | ``f``, ``∂ f`` |
+[Subgradient Method](@ref sec-subgradient-method) | [`subgradient_method`](@ref), [`SubGradientMethodState`](@ref) | ``f``, ``∂ f`` |
 [Stochastic Gradient Descent](@ref StochasticGradientDescentSolver) | [`stochastic_gradient_descent`](@ref), [`StochasticGradientDescentState`](@ref) | ``f = \sum_i f_i``, ``\operatorname{grad} f_i`` |
 [The Riemannian Trust-Regions Solver](@ref trust_regions) | [`trust_regions`](@ref), [`TrustRegionsState`](@ref) | ``f``, ``\operatorname{grad} f``, ``\operatorname{Hess} f`` |
 

diff --git a/docs/src/solvers/subgradient.md b/docs/src/solvers/subgradient.md
@@ -1,4 +1,4 @@
-# [Subgradient method](@idsec-subgradient-method)
+# [Subgradient method](@id sec-subgradient-method)
 
 ```@docs
 subgradient_method
-Original file line number
+Diff line change
@@ Expand Up / @@ -20,7 +20,6 @@ InplaceEvaluation @@
     evaluation_type
     ```
     ## Decorators for objectives
     An objective can be decorated using the following trait and function to initialize
@@ Expand Down @@