intoduce sub_kwargs also for trust regions.

Changelog.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [0.4.45] unreleased
 
+### Added
+
+* Introduce `sub_kwargs` and `sub_stopping_criterion` for `trust_regions` as noticed in [#336](https://github.com/JuliaManifolds/Manopt.jl/discussions/336)
+
 ### Changed
 
 * Faster `safe_indices` in L-BFGS.

src/solvers/augmented_Lagrangian_method.jl

@@ -84,6 +84,7 @@ mutable struct AugmentedLagrangianMethodState{
         stopping_criterion::SC=StopAfterIteration(300) | (
             StopWhenSmallerOrEqual(:ϵ, ϵ_min) & StopWhenChangeLess(1e-10)
         ),
+        kwargs...,
     ) where {
         P,
         Pr<:AbstractManoptProblem,
@@ -344,6 +345,7 @@ function augmented_Lagrangian_method!(
             ),
             stopping_criterion=sub_stopping_criterion,
             stepsize=default_stepsize(M, QuasiNewtonState),
+            sub_kwargs...,
         );
         sub_kwargs...,
     ),

src/solvers/truncated_conjugate_gradient_descent.jl

@@ -71,6 +71,7 @@ mutable struct TruncatedConjugateGradientState{T,R<:Real,SC<:StoppingCriterion,P
                                               StopWhenTrustRegionIsExceeded() |
                                               StopWhenCurvatureIsNegative() |
                                               StopWhenModelIncreased(),
+        kwargs...,
     ) where {T,R<:Real,F}
         tcgs = new{T,R,typeof(stopping_criterion),F}()
         tcgs.stop = stopping_criterion

src/solvers/trust_regions.jl

@@ -277,37 +277,42 @@ by default the [`truncated_conjugate_gradient_descent`](@ref) is used.
 * `Hess_f` – (optional), the hessian ``\operatorname{Hess}F(x): T_x\mathcal M → T_x\mathcal M``, ``X ↦ \operatorname{Hess}F(x)[X] = ∇_ξ\operatorname{grad}f(x)``
 * `p`      – (`rand(M)`) an initial value ``x  ∈  \mathcal M``
 
-# Optional
+# Keyword Arguments
+
+* `acceptance_rate`         – Accept/reject threshold: if ρ (the performance ratio for the
+  iterate) is at least the acceptance rate ρ', the candidate is accepted.
+  This value should  be between ``0`` and ``\frac{1}{4}``
+  (formerly this was called `ρ_prime, which will be removed on the next breaking change)
+* `augmentation_threshold`  – (`0.75`) trust-region augmentation threshold: if ρ is above this threshold,
+  we have a solution on the trust region boundary and negative curvature, we extend (augment) the radius
+* `augmentation_factor`     – (`2.0`) trust-region augmentation factor
 * `evaluation`              – ([`AllocatingEvaluation`](@ref)) specify whether the gradient and hessian work by
    allocation (default) or [`InplaceEvaluation`](@ref) in place
+* `κ`                       – (`0.1`) the linear convergence target rate of the tCG method
+    [`truncated_conjugate_gradient_descent`](@ref), and is used in a stopping crierion therein
 * `max_trust_region_radius` – the maximum trust-region radius
 * `preconditioner`          – a preconditioner (a symmetric, positive definite operator
   that should approximate the inverse of the Hessian)
-* `randomize`               – set to true if the trust-region solve is to be initiated with a
-  random tangent vector and no preconditioner will be used.
 * `project!`                – (`copyto!`) specify a projection operation for tangent vectors
   within the subsolver for numerical stability.
   this means we require a function `(M, Y, p, X) -> ...` working in place of `Y`.
+* `randomize`               – set to true if the trust-region solve is to be initiated with a
+  random tangent vector and no preconditioner will be used.
+* `ρ_regularization`        – (`1e3`) regularize the performance evaluation ``ρ``
+  to avoid numerical inaccuracies.
+* `reduction_factor`        – (`0.25`) trust-region reduction factor
+* `reduction_threshold`     – (`0.1`) trust-region reduction threshold: if ρ is below this threshold,
+  the trust region radius is reduced by `reduction_factor`.
 * `retraction` – (`default_retraction_method(M, typeof(p))`) a retraction to use
 * `stopping_criterion`      – ([`StopAfterIteration`](@ref)`(1000) | `[`StopWhenGradientNormLess`](@ref)`(1e-6)`) a functor inheriting
   from [`StoppingCriterion`](@ref) indicating when to stop.
-* `trust_region_radius`     – the initial trust-region radius
-* `acceptance_rate`         – Accept/reject threshold: if ρ (the performance ratio for the
-  iterate) is at least the acceptance rate ρ', the candidate is accepted.
-  This value should  be between ``0`` and ``\frac{1}{4}``
-  (formerly this was called `ρ_prime, which will be removed on the next breaking change)
-* `ρ_regularization`        – (`1e3`) regularize the performance evaluation ``ρ``
-  to avoid numerical inaccuracies.
+* `sub_kwargs`              – keyword arguments passed to the sub state and used to decorate the sub options, e.g. with debug.
+* `sub_stopping_criterion`  – a stopping criterion for the sub solver, uses the same standard as TCG.
+* `sub_problem`             – ([`DefaultManoptProblem`](@ref)`(M, `[`ConstrainedManifoldObjective`](@ref)`(subcost, subgrad; evaluation=evaluation))`) problem for the subsolver
+* `sub_state`               – ([`QuasiNewtonState`](@ref)) using [`QuasiNewtonLimitedMemoryDirectionUpdate`](@ref) with [`InverseBFGS`](@ref) and `sub_stopping_criterion` as a stopping criterion. See also `sub_kwargs`.
 * `θ`                       – (`1.0`) 1+θ is the superlinear convergence target rate of the tCG-method
     [`truncated_conjugate_gradient_descent`](@ref), and is used in a stopping crierion therein
-* `κ`                       – (`0.1`) the linear convergence target rate of the tCG method
-    [`truncated_conjugate_gradient_descent`](@ref), and is used in a stopping crierion therein
-* `reduction_threshold`     – (`0.1`) trust-region reduction threshold: if ρ is below this threshold,
-  the trust region radius is reduced by `reduction_factor`.
-* `reduction_factor`        – (`0.25`) trust-region reduction factor
-* `augmentation_threshold`  – (`0.75`) trust-region augmentation threshold: if ρ is above this threshold,
-  we have a solution on the trust region boundary and negative curvature, we extend (augment) the radius
-* `augmentation_factor`     – (`2.0`) trust-region augmentation factor
+* `trust_region_radius`     – the initial trust-region radius
 
 For the case that no hessian is provided, the Hessian is computed using finite difference, see
 [`ApproxHessianFiniteDifference`](@ref).
@@ -493,16 +498,29 @@ 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_problem=DefaultManoptProblem(TangentSpace(M, p), sub_objective),
-    sub_state::Union{AbstractHessianSolverState,AbstractEvaluationType}=TruncatedConjugateGradientState(
-        TangentSpace(M, copy(M, p)),
-        zero_vector(M, p);
-        θ=θ,
-        κ=κ,
-        trust_region_radius,
-        randomize=randomize,
-        (project!)=project!,
+    sub_stopping_criterion::StoppingCriterion=StopAfterIteration(manifold_dimension(M)) |
+                                              StopWhenResidualIsReducedByFactorOrPower(;
+                                                  κ=κ, θ=θ
+                                              ) |
+                                              StopWhenTrustRegionIsExceeded() |
+                                              StopWhenCurvatureIsNegative() |
+                                              StopWhenModelIncreased(),
+    sub_state::AbstractManoptSolverState=decorate_state!(
+        TruncatedConjugateGradientState(
+            TangentSpace(M, copy(M, p)),
+            zero_vector(M, p);
+            θ=θ,
+            κ=κ,
+            trust_region_radius,
+            randomize=randomize,
+            (project!)=project!,
+            sub_kwargs...,
+            stopping_criterion=sub_stopping_criterion,
+        );
+        sub_kwargs...,
     ),
     kwargs..., #collect rest
 ) where {Proj,O<:Union{ManifoldHessianObjective,AbstractDecoratedManifoldObjective},R}