Unify definition and usage of sub_kwargs in all 7 solvers with sub so…

…lvers.

src/solvers/FrankWolfe.jl

@@ -180,7 +180,7 @@ the [`AbstractManifoldGradientObjective`](@ref) `gradient_objective` directly.
   For points 2 and 3 the `sub_state` has to be set to the corresponding [`AbstractEvaluationType`](@ref), [`AllocatingEvaluation`](@ref) and [`InplaceEvaluation`](@ref), respectively
 * `sub_state`          - (`evaluation` if `sub_problem` is a function, a decorated [`GradientDescentState`](@ref) otherwise)
   for a function, the evaluation is inherited from the Frank-Wolfe `evaluation` keyword.
-* `sub_kwargs`         - (`[]`) – keyword arguments to decorate the `sub_state` default state in case the sub_problem is not a function
+* `sub_kwargs`         - (`(;)`) – keyword arguments to decorate the `sub_state` default state in case the sub_problem is not a function
 
 All other keyword arguments are passed to [`decorate_state!`](@ref) for decorators or
 [`decorate_objective!`](@ref), respectively.
@@ -259,7 +259,7 @@ function Frank_Wolfe_method!(
                               StopWhenChangeLess(1.0e-8),
     sub_cost=FrankWolfeCost(p, initial_vector),
     sub_grad=FrankWolfeGradient(p, initial_vector),
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_objective=ManifoldGradientObjective(sub_cost, sub_grad),
     sub_problem=DefaultManoptProblem(
         M,
@@ -278,6 +278,7 @@ function Frank_Wolfe_method!(
                 stepsize=default_stepsize(
                     M, GradientDescentState; retraction_method=retraction_method
                 ),
+                sub_kwargs...,
             );
             objective_type=objective_type,
             sub_kwargs...,

src/solvers/adaptive_regularization_with_cubics.jl

@@ -391,15 +391,19 @@ function adaptive_regularization_with_cubics!(
     γ1::R=0.1,
     γ2::R=2.0,
     θ::R=0.5,
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_stopping_criterion::StoppingCriterion=StopAfterIteration(maxIterLanczos) |
                                               StopWhenFirstOrderProgress(θ),
-    sub_state::Union{<:AbstractManoptSolverState,<:AbstractEvaluationType}=LanczosState(
-        TangentSpace(M, copy(M, p));
-        maxIterLanczos=maxIterLanczos,
-        σ=σ,
-        θ=θ,
-        stopping_criterion=sub_stopping_criterion,
+    sub_state::Union{<:AbstractManoptSolverState,<:AbstractEvaluationType}=decorate_state!(
+        LanczosState(
+            TangentSpace(M, copy(M, p));
+            maxIterLanczos=maxIterLanczos,
+            σ=σ,
+            θ=θ,
+            stopping_criterion=sub_stopping_criterion,
+            sub_kwargs...,
+        );
+        sub_kwargs,
     ),
     sub_objective=nothing,
     sub_problem=nothing,
@@ -414,7 +418,9 @@ function adaptive_regularization_with_cubics!(
 ) where {T,R,O<:Union{ManifoldHessianObjective,AbstractDecoratedManifoldObjective}}
     dmho = decorate_objective!(M, mho; objective_type=objective_type, kwargs...)
     if isnothing(sub_objective)
-        sub_objective = AdaptiveRagularizationWithCubicsModelObjective(dmho, σ)
+        sub_objective = decorate_objective!(
+            M, AdaptiveRagularizationWithCubicsModelObjective(dmho, σ); sub_kwargs...
+        )
     end
     if isnothing(sub_problem)
         sub_problem = DefaultManoptProblem(TangentSpace(M, copy(M, p)), sub_objective)

src/solvers/augmented_Lagrangian_method.jl

@@ -331,7 +331,7 @@ function augmented_Lagrangian_method!(
     objective_type=:Riemannian,
     sub_cost=AugmentedLagrangianCost(cmo, ρ, μ, λ),
     sub_grad=AugmentedLagrangianGrad(cmo, ρ, μ, λ),
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_stopping_criterion=StopAfterIteration(300) |
                            StopWhenGradientNormLess(ϵ) |
                            StopWhenStepsizeLess(1e-8),

src/solvers/difference-of-convex-proximal-point.jl

@@ -191,7 +191,7 @@ difference_of_convex_proximal_point(M, grad_h, p0; g=g, grad_g=grad_g)
   This is generated by default when `grad_g` is provided. You can specify your own by overwriting this keyword.
 * `sub_hess`               – (a finite difference approximation by default) specify
   a Hessian of the subproblem, which the default solver, see `sub_state` needs
-* `sub_kwargs`             – (`[]`) pass keyword arguments to the `sub_state`, in form of
+* `sub_kwargs`             – (`(;)`) pass keyword arguments to the `sub_state`, in form of
   a `Dict(:kwname=>value)`, unless you set the `sub_state` directly.
 * `sub_objective`          – (a gradient or hessian objective based on the last 3 keywords)
   provide the objective used within `sub_problem` (if that is not specified by the user)
@@ -331,7 +331,7 @@ function difference_of_convex_proximal_point!(
         ProximalDCGrad(grad_g, copy(M, p), λ(1); evaluation=evaluation)
     end,
     sub_hess=ApproxHessianFiniteDifference(M, copy(M, p), sub_grad; evaluation=evaluation),
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_stopping_criterion=StopAfterIteration(300) | StopWhenGradientNormLess(1e-8),
     sub_objective=if isnothing(sub_cost) || isnothing(sub_grad)
         nothing
@@ -368,7 +368,7 @@ function difference_of_convex_proximal_point!(
         decorate_state!(
             if isnothing(sub_hess)
                 GradientDescentState(
-                    M, copy(M, p); stopping_criterion=sub_stopping_criterion
+                    M, copy(M, p); stopping_criterion=sub_stopping_criterion, sub_kwargs...
                 )
             else
                 TrustRegionsState(
@@ -378,7 +378,7 @@ function difference_of_convex_proximal_point!(
                         TangentSpace(M, copy(M, p)),
                         TrustRegionModelObjective(sub_objective),
                     ),
-                    TruncatedConjugateGradientState(TangentSpace(M, p)),
+                    TruncatedConjugateGradientState(TangentSpace(M, p); sub_kwargs...),
                 )
             end;
             sub_kwargs...,

src/solvers/difference_of_convex_algorithm.jl

@@ -166,7 +166,7 @@ difference_of_convex_algorithm(M, f, g, grad_h, p; grad_g=grad_g)
   This is generated by default when `grad_g` is provided. You can specify your own by overwriting this keyword.
 * `sub_hess`              – (a finite difference approximation by default) specify a Hessian
    of the subproblem, which the default solver, see `sub_state` needs
-* `sub_kwargs`            - (`[]`) pass keyword arguments to the `sub_state`, in form of
+* `sub_kwargs`            - (`(;)`) pass keyword arguments to the `sub_state`, in form of
   a `Dict(:kwname=>value)`, unless you set the `sub_state` directly.
 * `sub_objective`         - (a gradient or hessian objective based on the last 3 keywords)
   provide the objective used within `sub_problem` (if that is not specified by the user)
@@ -301,7 +301,7 @@ function difference_of_convex_algorithm!(
         )
     end,
     sub_hess=ApproxHessianFiniteDifference(M, copy(M, p), sub_grad; evaluation=evaluation),
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_stopping_criterion=StopAfterIteration(300) | StopWhenGradientNormLess(1e-8),
     sub_objective=if isnothing(sub_cost) || isnothing(sub_grad)
         nothing
@@ -333,11 +333,15 @@ function difference_of_convex_algorithm!(
         decorate_state!(
             if isnothing(sub_hess)
                 GradientDescentState(
-                    M, copy(M, p); stopping_criterion=sub_stopping_criterion
+                    M, copy(M, p); stopping_criterion=sub_stopping_criterion, sub_kwargs...
                 )
             else
                 TrustRegionsState(
-                    M, copy(M, p), sub_objective; stopping_criterion=sub_stopping_criterion
+                    M,
+                    copy(M, p),
+                    sub_objective;
+                    stopping_criterion=sub_stopping_criterion,
+                    sub_kwargs...,
                 )
             end;
             sub_kwargs...,

src/solvers/exact_penalty_method.jl

@@ -301,7 +301,7 @@ function exact_penalty_method!(
     smoothing=LogarithmicSumOfExponentials(),
     sub_cost=ExactPenaltyCost(cmo, ρ, u; smoothing=smoothing),
     sub_grad=ExactPenaltyGrad(cmo, ρ, u; smoothing=smoothing),
-    sub_kwargs=[],
+    sub_kwargs=(;),
     sub_problem::AbstractManoptProblem=DefaultManoptProblem(
         M,
         decorate_objective!(
@@ -324,6 +324,7 @@ function exact_penalty_method!(
             ),
             stopping_criterion=sub_stopping_criterion,
             stepsize=default_stepsize(M, QuasiNewtonState),
+            sub_kwargs...,
         );
         sub_kwargs...,
     ),

src/solvers/trust_regions.jl

@@ -498,8 +498,8 @@ function trust_regions!(
     reduction_factor::R=0.25,
     augmentation_threshold::R=0.75,
     augmentation_factor::R=2.0,
-    sub_kwargs=[],
-    sub_objective=TrustRegionModelObjective(mho),
+    sub_kwargs=(;),
+    sub_objective=decorate_objective!(M, TrustRegionModelObjective(mho), sub_kwargs...),
     sub_problem=DefaultManoptProblem(TangentSpace(M, p), sub_objective),
     sub_stopping_criterion::StoppingCriterion=StopAfterIteration(manifold_dimension(M)) |
                                               StopWhenResidualIsReducedByFactorOrPower(;