Update broken test for OptimizationManopt

SciML · Jun 13, 2024 · 8420994 · 8420994
1 parent a0dfc18
commit 8420994
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Showing 2 changed files with 10 additions and 5 deletions.
diff --git a/lib/OptimizationManopt/src/OptimizationManopt.jl b/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...)

diff --git a/lib/OptimizationManopt/test/runtests.jl b/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())