A few final tweaks to the docs.

Changelog.md

@@ -5,7 +5,7 @@ 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).
 
-# [0.5.0] unreleased
+# [0.5.0] – August 29, 2024
 
 This breaking update is mainly concerned with improving a unified experience through all solvers
 and some usability improvements, such that for example the different gradient update rules are easier to specify.

docs/src/about.md

@@ -8,13 +8,13 @@ This Julia package was started and is currently maintained by [Ronny Bergmann](h
 Thanks to the following contributors to `Manopt.jl`:
 
 * [Constantin Ahlmann-Eltze](https://const-ae.name) implemented the [gradient and differential `check` functions](helpers/checks.md)
-* [Renée Dornig](https://github.com/r-dornig) implemented the [particle swarm](solvers/particle_swarm.md), the [Riemannian Augmented Lagrangian Method](solvers/augmented_Lagrangian_method.md), the [Exact Penalty Method](solvers/exact_penalty_method.md), as well as the [`NonmonotoneLinesearch`](@ref)
+* [Renée Dornig](https://github.com/r-dornig) implemented the [particle swarm](solvers/particle_swarm.md), the [Riemannian Augmented Lagrangian Method](solvers/augmented_Lagrangian_method.md), the [Exact Penalty Method](solvers/exact_penalty_method.md), as well as the [`NonmonotoneLinesearch`](@ref). These solvers are also the first one with modular/exchangable sub solvers.
 * [Willem Diepeveen](https://www.maths.cam.ac.uk/person/wd292) implemented the [primal-dual Riemannian semismooth Newton](solvers/primal_dual_semismooth_Newton.md) solver.
-* [Hajg Jasa](https://www.ntnu.edu/employees/hajg.jasa) implemented the [convex bundle method](solvers/convex_bundle_method.md) and the [proximal bundle method](solvers/proximal_bundle_method.md).
-* Even Stephansen Kjemsås contributed to the implementation of the [Frank Wolfe Method](solvers/FrankWolfe.md) solver
-* Mathias Ravn Munkvold contributed most of the implementation of the [Adaptive Regularization with Cubics](solvers/adaptive-regularization-with-cubics.md) solver
-* [Tom-Christian Riemer](https://www.tu-chemnitz.de/mathematik/wire/mitarbeiter.php) implemented the [trust regions](solvers/trust_regions.md) and [quasi Newton](solvers/quasi_Newton.md) solvers.
-* [Markus A. Stokkenes](https://www.linkedin.com/in/markus-a-stokkenes-b41bba17b/) contributed most of the implementation of the [Interior Point Newton Method](solvers/interior_point_Newton.md)
+* [Hajg Jasa](https://www.ntnu.edu/employees/hajg.jasa) implemented the [convex bundle method](solvers/convex_bundle_method.md) and the [proximal bundle method](solvers/proximal_bundle_method.md) and a default subsolver each of them.
+* Even Stephansen Kjemsås contributed to the implementation of the [Frank Wolfe Method](solvers/FrankWolfe.md) solver.
+* Mathias Ravn Munkvold contributed most of the implementation of the [Adaptive Regularization with Cubics](solvers/adaptive-regularization-with-cubics.md) solver as well as ist [Lanczos](@ref arc-Lanczos) subsolver
+* [Tom-Christian Riemer](https://www.tu-chemnitz.de/mathematik/wire/mitarbeiter.php) implemented the [trust regions](solvers/trust_regions.md) and [quasi Newton](solvers/quasi_Newton.md) solvers as well as the [truncated conjugate gradient descent](solvers/truncated_conjugate_gradient_descent.md) subsolver.
+* [Markus A. Stokkenes](https://www.linkedin.com/in/markus-a-stokkenes-b41bba17b/) contributed most of the implementation of the [Interior Point Newton Method](solvers/interior_point_Newton.md) as well as its default [Conjugate Residual](solvers/conjugate_residual.md) subsolver
 * [Manuel Weiss](https://scoop.iwr.uni-heidelberg.de/author/manuel-weiß/) implemented most of the [conjugate gradient update rules](@ref cg-coeffs)
 
 as well as various [contributors](https://github.com/JuliaManifolds/Manopt.jl/graphs/contributors) providing small extensions, finding small bugs and mistakes and fixing them by opening [PR](https://github.com/JuliaManifolds/Manopt.jl/pulls)s. Thanks to all of you.

docs/src/plans/stepsize.md

@@ -20,10 +20,14 @@ on the manifold currently under consideration.
 Currently, the following step sizes are available
 
 ```@docs
+AdaptiveWNGradient
 ArmijoLinesearch
 ConstantLength
 DecreasingLength
+NonmonotoneLinesearch
 Polyak
+WolfePowellLinesearch
+WolfePowellBinaryLinesearch
 ```
 
 Some step sizes use [`max_stepsize`](@ref) function as a rough upper estimate for the trust region size.
@@ -41,7 +45,7 @@ Modules = [Manopt]
 Pages = ["plans/stepsize.jl"]
 Private = true
 Order = [:function, :type]
-Filter = t -> !(t in [Stepsize, ArmijoLinesearch, ConstantLength, DecreasingLength, Polyak])
+Filter = t -> !(t in [Stepsize, AdaptiveWNGradient, ArmijoLinesearch, ConstantLength, DecreasingLength, NonmonotoneLinesearch, Polyak, WolfePowellLinesearch, WolfePowellBinaryLinesearch ])
 ```
 
 

docs/src/solvers/adaptive-regularization-with-cubics.md

@@ -21,7 +21,7 @@ AdaptiveRegularizationState
 
 There are several ways to approach the subsolver. The default is the first one.
 
-## Lanczos iteration
+## [Lanczos iteration](@id arc-Lanczos)
 
 ```@docs
 Manopt.LanczosState

docs/src/solvers/gradient_descent.md

@@ -20,10 +20,10 @@ GradientDescentState
 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
 
 ```@docs
+AverageGradient
 DirectionUpdateRule
 IdentityUpdateRule
 MomentumGradient
-AverageGradient
 Nesterov
 ```
 

src/solvers/cyclic_proximal_point.jl

@@ -16,8 +16,8 @@ function show(io::IO, cpps::CyclicProximalPointState)
     return print(io, s)
 end
 _doc_CPPA = """
-    cyclic_proximal_point(M, f, proxes_f; kwargs...)
-    cyclic_proximal_point(M, mpo; kwargs...)
+    cyclic_proximal_point(M, f, proxes_f, p; kwargs...)
+    cyclic_proximal_point(M, mpo, p; kwargs...)
     cyclic_proximal_point!(M, f, proxes_f; kwargs...)
     cyclic_proximal_point!(M, mpo; kwargs...)
 

src/solvers/proximal_bundle_method.jl

@@ -216,7 +216,7 @@ with ``X_{q_j} ∈ ∂f(q_j)``, ``p_k`` the last serious iterate,
 sub solver, see for example the [`proximal_bundle_method_subsolver`](@ref).
 """
 _doc_PBM = """
-    proximal_bundle_method(M, f, ∂f, p, kwargs...)
+    proximal_bundle_method(M, f, ∂f, p=rand(M), kwargs...)
     proximal_bundle_method!(M, f, ∂f, p, kwargs...)
 
 perform a proximal bundle method ``p^{(k+1)} = $(_tex(:retr))_{p^{(k)}}(-d_k)``,
@@ -259,7 +259,7 @@ $(_note(:OutputSection))
 
 @doc "$(_doc_PBM)"
 function proximal_bundle_method(
-    M::AbstractManifold, f::TF, ∂f::TdF, p; kwargs...
+    M::AbstractManifold, f::TF, ∂f::TdF, p=rand(M); kwargs...
 ) where {TF,TdF}
     p_star = copy(M, p)
     return proximal_bundle_method!(M, f, ∂f, p_star; kwargs...)

src/solvers/quasi_Newton.jl

@@ -234,7 +234,7 @@ function quasi_Newton(
     M::AbstractManifold,
     f::TF,
     grad_f::TDF,
-    p;
+    p=rand(M);
     evaluation::AbstractEvaluationType=AllocatingEvaluation(),
     kwargs...,
 ) where {TF,TDF}