Update base point properly.

Author: kellertuer

docs/src/plans/objective.md

@@ -249,5 +249,6 @@ AbstractManifoldSubObjective
 ```@docs
 Manopt.get_objective_cost
 Manopt.get_objective_gradient
-Manopt.get_objective_Hessian
+Manopt.get_objective_hessian
+Manopt.get_objective_preconditioner
 ```

src/plans/problem.jl

@@ -95,6 +95,10 @@ function set_manopt_parameter!(amp::AbstractManoptProblem, ::Val{:Manifold}, arg
     set_manopt_parameter!(get_manifold(amp), args...)
     return amp
 end
+function set_manopt_parameter!(TpM::TangentSpace, ::Val{:Basepoint}, p)
+    copyto!(TpM.manifold, TpM.point, p)
+    return TpM
+end
 
 function set_manopt_parameter!(amp::AbstractManoptProblem, ::Val{:Objective}, args...)
     set_manopt_parameter!(get_objective(amp), args...)

src/solvers/trust_regions.jl

@@ -565,9 +565,8 @@ function step_solver!(mp::AbstractManoptProblem, trs::TrustRegionsState, i)
     # Solve TR subproblem – update options
     # TODO provide these setters for the sub problem / sub state
     # set_paramater!(trs.sub_problem, :Basepoint, trs.p)
-    set_manopt_parameter!(trs.sub_state, :Basepoint, trs.p)
-    set_manopt_parameter!(trs.sub_problem, :Basepoint, trs.p)
-    set_manopt_parameter!(trs.sub_state, :Iterate, trs.Y)
+    set_manopt_parameter!(trs.sub_problem, :Manifold, :Basepoint, copy(M, trs.p))
+    set_manopt_parameter!(trs.sub_state, :Iterate, copy(M, trs.p, trs.Y))
     set_manopt_parameter!(trs.sub_state, :TrustRegionRadius, trs.trust_region_radius)
     solve!(trs.sub_problem, trs.sub_state)
     #

test/solvers/test_difference_of_convex.jl

@@ -129,7 +129,7 @@ import Manifolds: inner
             M, grad_h!, p0; g=g, grad_g=grad_g!, evaluation=InplaceEvaluation()
         )
         p5 = difference_of_convex_proximal_point(M, grad_h, p0; g=g, grad_g=grad_g)
-        #p5b = difference_of_convex_proximal_point(M, grad_h; g=g, grad_g=grad_g)
+        p5b = difference_of_convex_proximal_point(M, grad_h; g=g, grad_g=grad_g)
         # using gradient descent
         p5c = difference_of_convex_proximal_point(
             M,
@@ -150,12 +150,12 @@ import Manifolds: inner
         p6 = get_solver_result(s2)
         @test Manopt.get_message(s2) == ""
 
-        @test_broken isapprox(M, p3, p4)
+        @test isapprox(M, p3, p4)
         @test isapprox(M, p4, p5)
         @test isapprox(M, p5, p6)
-        # @test isapprox(f(M, p5b), 0.0; atol=2e-16) # bit might be a different min due to rand
-        @test isapprox(f(M, p5c), 0.0; atol=1e-9) # might be a bit imprecise
-        @test_broken isapprox(f(M, p4), 0.0; atol=1e-8) # might be a bit imprecise
+        @test isapprox(f(M, p5b), 0.0; atol=2e-16) # bit might be a different min due to rand
+        @test isapprox(f(M, p5c), 0.0; atol=1e-10)
+        @test isapprox(f(M, p4), 0.0; atol=1e-14)
 
         Random.seed!(23)
         p7 = difference_of_convex_algorithm(M, f, g, grad_h; grad_g=grad_g)
@@ -169,7 +169,7 @@ import Manifolds: inner
         p9 = difference_of_convex_algorithm(
             M, f, g, grad_h, p0; grad_g=grad_g, sub_hess=nothing
         )
-        @test_broken isapprox(M, p9, p2; atol=1e-7)
+        @test isapprox(M, p9, p2; atol=1e-9)
 
         @test_throws ErrorException difference_of_convex_proximal_point(
             M, grad_h, p0; sub_problem=nothing

test/solvers/test_trust_regions.jl

@@ -359,24 +359,22 @@ include("../utils/example_tasks.jl")
     end
     @testset "Euclidean Embedding" begin
         Random.seed!(42)
-        n = 5
+        n = 2
         A = Symmetric(randn(n + 1, n + 1))
         # Euclidean variant with conversion
         M = Sphere(n)
-        p0 = rand(M)
+        p0 = [1.0, zeros(n)...]
         f(E, p) = p' * A * p
         ∇f(E, p) = A * p
         ∇²f(M, p, X) = A * X
         λ = min(eigvals(A)...)
-        q = trust_regions(M, f, ∇f, p0; objective_type=:Euclidean)
-        q2 = trust_regions(M, f, ∇f, ∇²f, p0; objective_type=:Euclidean)
-        @test f(M, q) ≈ λ atol = 2 * 1e-1
-        @test_broken f(M, q) ≈ f(M, q2)
+        q = trust_regions(M, f, ∇f, p0; objective_type=:Euclidean, (project!)=project!)
+        @test f(M, q) ≈ λ atol = 1 * 1e-1 # a bit inprecise?
         grad_f(M, p) = A * p - (p' * A * p) * p
         Hess_f(M, p, X) = A * X - (p' * A * X) .* p - (p' * A * p) .* X
         q3 = trust_regions(M, f, grad_f, p0)
         q4 = trust_regions(M, f, grad_f, Hess_f, p0)
-        @test f(M, q) ≈ f(M, q3) atol = 5 * 1e-1 # A bit imprecise?
-        @test f(M, q) ≈ f(M, q4) atol = 5 * 1e-1 # A bit imprecise?
+        @test f(M, q3) ≈ λ atol = 5 * 1e-8
+        @test f(M, q4) ≈ λ atol = 5 * 1e-10
     end
 end