Fixing stuff for Documenter 1.3 ...

docs/src/functions/adjoint_differentials.md

@@ -8,6 +8,6 @@ Pages   = ["adjoint_differentials.jl"]
 # Literature
 
 ```@bibliography
-Pages = ["functions/adjoint_differentials.md"]
+Pages = ["adjoint_differentials.md"]
 Canonical=false
 ```

docs/src/functions/bezier.md

@@ -8,6 +8,6 @@ Pages   = ["bezier_curves.jl"]
 # Literature
 
 ```@bibliography
-Pages = ["functions/bezier.md"]
+Pages = ["bezier.md"]
 Canonical=false
 ```

docs/src/functions/gradients.md

@@ -19,6 +19,6 @@ Pages   = ["gradients.jl"]
 # Literature
 
 ```@bibliography
-Pages = ["functions/gradients.md"]
+Pages = ["gradients.md"]
 Canonical=false
 ```

docs/src/functions/proximal_maps.md

@@ -29,6 +29,6 @@ Pages   = ["proximal_maps.jl"]
 # Literature
 
 ```@bibliography
-Pages = ["functions/proximal_maps.md"]
+Pages = ["proximal_maps.md"]
 Canonical=false
 ```

docs/src/references.bib

@@ -215,7 +215,7 @@ @book{doCarmo:1992
     TITLE     = {Riemannian Geometry},
     AUTHOR    = {do Carmo, Manfredo Perdigão},
     YEAR      = {1992},
-    SERIES    = {Mathematics: Theory & Applications},
+    SERIES    = {Mathematics: Theory \& Applications},
     PAGES     = {xiv+300},
     PUBLISHER = {Birkhäuser Boston, Inc., Boston, MA},
     DOI       = {10.1007/978-1-4757-2201-7},
@@ -266,7 +266,7 @@ @article{DaiYuan:1999
     MONTH     = jan,
     NUMBER    = {1},
     PAGES     = {177--182},
-    PUBLISHER = {Society for Industrial & Applied Mathematics (SIAM)},
+    PUBLISHER = {Society for Industrial \& Applied Mathematics (SIAM)},
     TITLE     = {A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property},
     VOLUME    = {10},
     YEAR      = {1999}
@@ -279,7 +279,7 @@ @article{DiepeveenLellmann:2021
     NOTE      = {arXiv: [2102.10309](https://arxiv.org/abs/2102.10309)},
     NUMBER    = {4},
     PAGES     = {1565--1600},
-    PUBLISHER = {Society for Industrial & Applied Mathematics ({SIAM})},
+    PUBLISHER = {Society for Industrial \& Applied Mathematics ({SIAM})},
     TITLE     = {An Inexact Semismooth Newton Method on Riemannian Manifolds with Application to Duality-Based Total Variation Denoising},
     VOLUME    = {14},
     YEAR      = {2021},
@@ -307,7 +307,7 @@ @book{Fletcher:1987
     AUTHOR    = {Fletcher, R.},
     EDITION   = {2},
     LOCATION  = {Chichester},
-    PUBLISHER = {John Wiley & Sons Ltd.},
+    PUBLISHER = {John Wiley \& Sons Ltd.},
     SERIES    = {A Wiley-Interscience Publication},
     TITLE     = {Practical Methods of Optimization},
     YEAR      = {1987}
@@ -358,7 +358,7 @@ @article{HagerZhang:2005
     MONTH     = jan,
     NUMBER    = {1},
     PAGES     = {170--192},
-    PUBLISHER = {Society for Industrial & Applied Mathematics (SIAM)},
+    PUBLISHER = {Society for Industrial \& Applied Mathematics (SIAM)},
     TITLE     = {A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search},
     VOLUME    = {16},
     YEAR      = {2005}
@@ -467,7 +467,7 @@ @article{LiuBoumal:2019
     YEAR      = {2019},
     MONTH     = mar,
     DOI       = {10.1007/s00245-019-09564-3},
-    JOURNAL   = {Applied Mathematics & Optimization},
+    JOURNAL   = {Applied Mathematics \& Optimization},
     TITLE     = {Simple algorithms for optimization on Riemannian manifolds with constraints},
     URL       = {https://arxiv.org/abs/1901.10000}
 }

docs/src/solvers/ChambollePock.md

@@ -113,6 +113,6 @@ Manopt.update_prox_parameters!
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/ChambollePock.md"]
+Pages = ["ChambollePock.md"]
 Canonical=false
 ```

docs/src/solvers/DouglasRachford.md

@@ -65,5 +65,5 @@ For specific [`DebugAction`](@ref)s and [`RecordAction`](@ref)s see also
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/DouglasRachford.md"]
+Pages = ["DouglasRachford.md"]
 ```

docs/src/solvers/FrankWolfe.md

@@ -25,6 +25,6 @@ FrankWolfeGradient
 ```
 
 ```@bibliography
-Pages = ["solvers/FrankWolfe.md"]
+Pages = ["FrankWolfe.md"]
 Canonical=false
 ```

docs/src/solvers/LevenbergMarquardt.md

@@ -18,6 +18,6 @@ LevenbergMarquardtState
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/LevenbergMarquardt.md"]
+Pages = ["LevenbergMarquardt.md"]
 Canonical=false
 ```

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

@@ -57,6 +57,6 @@ StopWhenFirstOrderProgress
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/adaptive-regularization-with-cubics.md"]
+Pages = ["adaptive-regularization-with-cubics.md"]
 Canonical=false
 ```

docs/src/solvers/augmented_Lagrangian_method.md

@@ -25,6 +25,6 @@ AugmentedLagrangianGrad
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/augmented_Lagrangian_method.md"]
+Pages = ["augmented_Lagrangian_method.md"]
 Canonical=false
 ```

docs/src/solvers/conjugate_gradient_descent.md

@@ -35,6 +35,6 @@ SteepestDirectionUpdateRule
 # Literature
 
 ```@bibliography
-Pages = ["solvers/conjugate_gradient_descent.md"]
+Pages = ["conjugate_gradient_descent.md"]
 Canonical=false
 ```

docs/src/solvers/cyclic_proximal_point.md

@@ -42,6 +42,6 @@ RecordProximalParameter
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/cyclic_proximal_point.md"]
+Pages = ["cyclic_proximal_point.md"]
 Canonical=false
 ```

docs/src/solvers/difference_of_convex.md

@@ -58,6 +58,6 @@ get_subtrahend_gradient
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/difference_of_convex.md"]
+Pages = ["difference_of_convex.md"]
 Canonical=false
 ```

docs/src/solvers/exact_penalty_method.md

@@ -29,6 +29,6 @@ LogarithmicSumOfExponentials
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/exact_penalty_method.md"]
+Pages = ["exact_penalty_method.md"]
 Canonical=false
 ```

docs/src/solvers/gradient_descent.md

@@ -54,7 +54,7 @@ The [`gradient_descent`](@ref) solver requires the following functions of your m
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/gradient_descent.md"]
+Pages = ["gradient_descent.md"]
 Canonical=false
 
 Luenberger:1972

docs/src/solvers/particle_swarm.md

@@ -18,6 +18,6 @@ ParticleSwarmState
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/particle_swarm.md"]
+Pages = ["particle_swarm.md"]
 Canonical=false
 ```

docs/src/solvers/primal_dual_semismooth_Newton.md

@@ -75,6 +75,6 @@ PrimalDualSemismoothNewtonState
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/primal_dual_semismooth_Newton.md"]
+Pages = ["primal_dual_semismooth_Newton.md"]
 Canonical=false
 ```

docs/src/solvers/quasi_Newton.md

@@ -115,6 +115,6 @@ QuasiNewtonState
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/quasi_Newton.md"]
+Pages = ["quasi_Newton.md"]
 Canonical=false
 ```

docs/src/solvers/truncated_conjugate_gradient_descent.md

@@ -47,6 +47,6 @@ TrustRegionModelObjective
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/truncated_conjugate_gradient_descent.md"]
+Pages = ["truncated_conjugate_gradient_descent.md"]
 Canonical=false
 ```

docs/src/solvers/trust_regions.md

@@ -62,6 +62,6 @@ Manopt.AbstractApproxHessian
 ## Literature
 
 ```@bibliography
-Pages = ["solvers/trust_regions.md"]
+Pages = ["trust_regions.md"]
 Canonical=false
 ```

src/plans/subsolver_plan.jl

@@ -45,12 +45,12 @@ Evaluate the gradient of the (original) objective stored within the subobjective
 """
 function get_objective_gradient(
     M::AbstractManifold, amso::AbstractManifoldSubObjective{E,O}, p
-) where {E,O<:AbstractManifoldGradientObjective{E}}
+) where {E,O<:AbstractManifoldObjective{E}}
     return get_gradient(M, get_objective(amso), p)
 end
 function get_objective_gradient!(
     M::AbstractManifold, X, amso::AbstractManifoldSubObjective{E,O}, p
-) where {E,O<:AbstractManifoldGradientObjective{E}}
+) where {E,O<:AbstractManifoldObjective{E}}
     return get_gradient!(M, X, get_objective(amso), p)
 end
 
@@ -62,13 +62,14 @@ Evaluate the Hessian of the (original) objective stored within the subobjective
 """
 function get_objective_hessian(
     M::AbstractManifold, amso::AbstractManifoldSubObjective{E,O}, p, X
-) where {E,O<:AbstractManifoldHessianObjective{E}}
+) where {E,O<:AbstractManifoldObjective{E}}
     return get_hessian(M, get_objective(amso), p, X)
 end
 function get_objective_hessian!(
     M::AbstractManifold, Y, amso::AbstractManifoldSubObjective{E,O}, p, X
-) where {E,O<:AbstractManifoldHessianObjective{E}}
-    return get_hessian!(M, Y, get_objective(amso), p, X)
+) where {E,O<:AbstractManifoldObjective{E}}
+    get_hessian!(M, Y, get_objective(amso), p, X)
+    return Y
 end
 
 @doc raw"""

src/solvers/adaptive_regularization_with_cubics.jl

@@ -401,13 +401,8 @@ function adaptive_regularization_with_cubics!(
         θ=θ,
         stopping_criterion=sub_stopping_criterion,
     ),
-    sub_objective=decorate_objective!(
-        M,
-        AdaptiveRagularizationWithCubicsModelObjective(mho, σ);
-        objective_type=objective_type,
-        sub_kwargs...,
-    ),
-    sub_problem=DefaultManoptProblem(TangentSpace(M, copy(M, p)), sub_objective),
+    sub_objective=nothing,
+    sub_problem=nothing,
     stopping_criterion::StoppingCriterion=if sub_state isa LanczosState
         StopAfterIteration(40) |
         StopWhenGradientNormLess(1e-9) |
@@ -417,8 +412,14 @@ function adaptive_regularization_with_cubics!(
     end,
     kwargs...,
 ) where {T,R,O<:Union{ManifoldHessianObjective,AbstractDecoratedManifoldObjective}}
-    X = copy(M, p, initial_tangent_vector)
     dmho = decorate_objective!(M, mho; objective_type=objective_type, kwargs...)
+    if isnothing(sub_objective)
+        sub_objective = AdaptiveRagularizationWithCubicsModelObjective(dmho, σ)
+    end
+    if isnothing(sub_problem)
+        sub_problem = DefaultManoptProblem(TangentSpace(M, copy(M, p)), sub_objective)
+    end
+    X = copy(M, p, initial_tangent_vector)
     dmp = DefaultManoptProblem(M, dmho)
     arcs = AdaptiveRegularizationState(
         M,

tutorials/ConstrainedOptimization.qmd

@@ -254,7 +254,7 @@ maximum(g(M, w1))
 
 ````{=commonmark}
 ```@bibliography
-Pages = ["tutorials/ConstrainedOptimization.md"]
+Pages = ["ConstrainedOptimization.md"]
 Canonical=false
 ```
 ````

tutorials/EmbeddingObjectives.qmd

@@ -193,7 +193,7 @@ to a Riemannian one.
 
 ````{=commonmark}
 ```@bibliography
-Pages = ["tutorials/EmbeddingObjectives.md"]
+Pages = ["EmbeddingObjectives.md"]
 Canonical=false
 ```
 ````

tutorials/GeodesicRegression.qmd

@@ -515,7 +515,7 @@ Note that the geodesics from the data to the regression geodesic meet at a nearl
 
 ````{=commonmark}
 ```@bibliography
-Pages = ["tutorials/GeodesicRegression.md"]
+Pages = ["GeodesicRegression.md"]
 Canonical=false
 ```
 ````

tutorials/Optimize!.qmd

@@ -223,7 +223,7 @@ plot(x,y,xaxis=:log, label="CPPA Cost")
 
 ````{=commonmark}
 ```@bibliography
-Pages = ["tutorials/Optimize!.md"]
+Pages = ["Optimize!.md"]
 Canonical=false
 ```
 ````