avoid errors and warnings in OptimizationManopt tests

Author: SebastianM-C

lib/OptimizationManopt/Project.toml

@@ -13,6 +13,7 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 
 [extras]
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 RipQP = "1e40b3f8-35eb-4cd8-8edd-3e515bb9de08"
 QuadraticModels = "f468eda6-eac5-11e8-05a5-ff9e497bcd19"
@@ -25,6 +26,7 @@ Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 
 [compat]
 julia = "1.10"
+DifferentiationInterface = "0.7"
 Manopt = "0.5"
 OptimizationBase = "4"
 LinearAlgebra = "1.10"
@@ -35,4 +37,4 @@ Reexport = "1.2"
 SciMLBase = "2.122.1"
 
 [targets]
-test = ["Enzyme", "ForwardDiff", "FiniteDiff", "QuadraticModels", "Random", "ReverseDiff", "RipQP", "Test", "Zygote"]
+test = ["DifferentiationInterface", "Enzyme", "ForwardDiff", "FiniteDiff", "QuadraticModels", "Random", "ReverseDiff", "RipQP", "Test", "Zygote"]

lib/OptimizationManopt/src/OptimizationManopt.jl

@@ -236,7 +236,7 @@ function SciMLBase.requiresgradient(opt::Union{
 end
 function SciMLBase.requireshessian(opt::Union{
         AdaptiveRegularizationCubicOptimizer, TrustRegionsOptimizer})
-    false
+    true
 end
 
 function build_loss(f::OptimizationFunction, prob, cb)

lib/OptimizationManopt/test/runtests.jl

@@ -2,6 +2,7 @@ using OptimizationManopt
 using OptimizationBase
 using Manifolds
 using ForwardDiff, Zygote, Enzyme, FiniteDiff, ReverseDiff
+using DifferentiationInterface: SecondOrder
 using Manopt, RipQP, QuadraticModels
 using Test
 using SciMLBase
@@ -40,12 +41,12 @@ R2 = Euclidean(2)
         prob_forwarddiff = OptimizationProblem(
             optprob_forwarddiff, x0, p; manifold = R2, stepsize = stepsize)
         sol = OptimizationBase.solve(prob_forwarddiff, opt)
-        @test sol.minimum < 0.2
+        @test sol.objective < 0.2
 
         optprob_grad = OptimizationFunction(rosenbrock; grad = rosenbrock_grad!)
         prob_grad = OptimizationProblem(optprob_grad, x0, p; manifold = R2, stepsize = stepsize)
         sol = OptimizationBase.solve(prob_grad, opt)
-        @test sol.minimum < 0.2
+        @test sol.objective < 0.2
     end
 
     @testset "Nelder-Mead" begin
@@ -58,7 +59,7 @@ R2 = Euclidean(2)
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt)
-        @test sol.minimum < 0.7
+        @test sol.objective < 0.7
     end
 
     @testset "Conjugate gradient descent" begin
@@ -72,7 +73,7 @@ R2 = Euclidean(2)
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt, stepsize = stepsize)
-        @test sol.minimum < 0.5
+        @test sol.objective < 0.5
     end
 
     @testset "Quasi Newton" begin
@@ -89,7 +90,7 @@ R2 = Euclidean(2)
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt, callback = callback, maxiters = 30)
-        @test sol.minimum < 1e-14
+        @test sol.objective < 1e-14
     end
 
     @testset "Particle swarm" begin
@@ -102,7 +103,7 @@ R2 = Euclidean(2)
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt)
-        @test sol.minimum < 0.1
+        @test sol.objective < 0.1
     end
 
     @testset "CMA-ES" begin
@@ -115,7 +116,7 @@ R2 = Euclidean(2)
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt)
-        @test sol.minimum < 0.1
+        @test sol.objective < 0.1
     end
 
     @testset "ConvexBundle" begin
@@ -129,7 +130,7 @@ R2 = Euclidean(2)
 
         sol = OptimizationBase.solve(
             prob, opt, sub_problem = Manopt.convex_bundle_method_subsolver)
-        @test sol.minimum < 0.1
+        @test sol.objective < 0.1
     end
 
     # @testset "TruncatedConjugateGradientDescent" begin
@@ -142,7 +143,7 @@ R2 = Euclidean(2)
     #     prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
     #     sol = OptimizationBase.solve(prob, opt)
-    #     @test_broken sol.minimum < 0.1
+    #     @test_broken sol.objective < 0.1
     # end
 
     @testset "AdaptiveRegularizationCubic" begin
@@ -153,11 +154,12 @@ R2 = Euclidean(2)
 
         #TODO: This autodiff currently provides a Hessian that seem to not provide a Hessian
         # ARC Fails but also AD before that warns. So it passes _some_ hessian but a wrong one, even in format
-        optprob = OptimizationFunction(rosenbrock, AutoForwardDiff())
+        optprob = OptimizationFunction(rosenbrock, SecondOrder(AutoForwardDiff(), AutoForwardDiff()))
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt)
-        @test sol.minimum < 0.1
+        @test sol.objective < 0.1
+        @test SciMLBase.successful_retcode(sol) broken=true
     end
 
     @testset "TrustRegions" begin
@@ -168,11 +170,12 @@ R2 = Euclidean(2)
 
         #TODO: This autodiff currently provides a Hessian that seem to not provide a Hessian
         # TR Fails but also AD before that warns. So it passes _some_ hessian but a wrong one, even in format
-        optprob = OptimizationFunction(rosenbrock, AutoForwardDiff())
+        optprob = OptimizationFunction(rosenbrock, SecondOrder(AutoForwardDiff(), AutoForwardDiff()))
         prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
         sol = OptimizationBase.solve(prob, opt)
-        @test sol.minimum < 0.1
+        @test sol.objective < 0.1
+        @test SciMLBase.successful_retcode(sol) broken=true
     end
 
     @testset "Custom constraints" begin