Update broken test for OptimizationManopt

docs/Project.toml

@@ -10,6 +10,7 @@ Ipopt_jll = "9cc047cb-c261-5740-88fc-0cf96f7bdcc7"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
+Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"

docs/src/optimization_packages/manopt.md

@@ -36,8 +36,8 @@ function or `OptimizationProblem`.
 
 The Rosenbrock function on the Euclidean manifold can be optimized using the `GradientDescentOptimizer` as follows:
 
-```@example Manopt1
-using Optimization, OptimizationManopt, Manifolds
+```@example Manopt
+using Optimization, OptimizationManopt, Manifolds, LinearAlgebra
 rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
 p = [1.0, 100.0]
@@ -57,7 +57,7 @@ sol = Optimization.solve(prob, opt)
 
 The box-constrained Karcher mean problem on the SPD manifold with the Frank-Wolfe algorithm can be solved as follows:
 
-```@example Manopt2
+```@example Manopt
 M = SymmetricPositiveDefinite(5)
 m = 100
 σ = 0.005
@@ -99,7 +99,7 @@ This example is based on the [example](https://juliamanifolds.github.io/ManoptEx
 The following example is adapted from the Rayleigh Quotient example in ManoptExamples.jl.
 We solve the Rayleigh quotient problem on the Sphere manifold:
 
-```@example Manopt3
+```@example Manopt
 using Optimization, OptimizationManopt
 using Manifolds, LinearAlgebra
 using Manopt
@@ -120,7 +120,7 @@ sol = solve(prob, GradientDescentOptimizer())
 
 Let's check that this indeed corresponds to the minimum eigenvalue of the matrix `A`.
 
-```@example Manopt3
+```@example Manopt
 @show eigmin(A)
 @show sol.objective
 ```

lib/OptimizationManopt/src/OptimizationManopt.jl

@@ -394,6 +394,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
     local x, cur, state
 
     manifold = haskey(cache.solver_args, :manifold) ? cache.solver_args[:manifold] : nothing
+    gradF = haskey(cache.solver_args, :riemannian_grad) ? cache.solver_args[:riemannian_grad] : nothing
+    hessF = haskey(cache.solver_args, :riemannian_hess) ? cache.solver_args[:riemannian_hess] : nothing
 
     if manifold === nothing
         throw(ArgumentError("Manifold not specified in the problem for e.g. `OptimizationProblem(f, x, p; manifold = SymmetricPositiveDefinite(5))`."))
@@ -433,9 +435,13 @@ function SciMLBase.__solve(cache::OptimizationCache{
 
     _loss = build_loss(cache.f, cache, _cb)
 
-    gradF = build_gradF(cache.f, cur)
+    if gradF === nothing
+        gradF = build_gradF(cache.f, cur)
+    end
 
-    hessF = build_hessF(cache.f, cur)
+    if hessF === nothing
+        hessF = build_hessF(cache.f, cur)
+    end
 
     if haskey(solver_kwarg, :stopping_criterion)
         stopping_criterion = Manopt.StopWhenAny(solver_kwarg.stopping_criterion...)

lib/OptimizationManopt/test/runtests.jl

@@ -154,8 +154,8 @@ end
     optprob = OptimizationFunction(rosenbrock, AutoForwardDiff())
     prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
-    @test_broken Optimization.solve(prob, opt)
-    @test_broken sol.minimum < 0.1
+    sol = Optimization.solve(prob, opt)
+    @test sol.minimum < 0.1
 end
 
 @testset "TrustRegions" begin
@@ -207,7 +207,6 @@ end
     q = Matrix{Float64}(I, 5, 5) .+ 2.0
     data2 = [exp(M, q, σ * rand(M; vector_at = q)) for i in 1:m]
 
-    f(M, x, p = nothing) = sum(distance(M, x, data2[i])^2 for i in 1:m)
     f(x, p = nothing) = sum(distance(M, x, data2[i])^2 for i in 1:m)
 
     optf = OptimizationFunction(f, Optimization.AutoFiniteDiff())