Pass in manifold as kwarg to OptimizationProblem and throw error if not passes or doesn't match solver

Author: Vaibhavdixit02

lib/OptimizationManopt/src/OptimizationManopt.jl

@@ -65,9 +65,6 @@ struct NelderMeadOptimizer{
     M::TM
 end
 
-function NelderMeadOptimizer(M::AbstractManifold)
-    return NelderMeadOptimizer{typeof(M)}(M)
-end
 
 function call_manopt_optimizer(opt::NelderMeadOptimizer,
                                loss,
@@ -269,6 +266,12 @@ function SciMLBase.__solve(prob::OptimizationProblem,
                            kwargs...)
     local x, cur, state
 
+    manifold = haskey(prob.kwargs, :manifold) ? prob.kwargs[:manifold] : nothing
+
+    if manifold === nothing || manifold !== opt.M
+        throw(ArgumentError("Either manifold not specified in the problem `OptimizationProblem(f, x, p; manifold = SymmetricPositiveDefinite(5))` or it doesn't match the manifold specified in the optimizer `$(opt.M)`"))
+    end
+
     if data !== Optimization.DEFAULT_DATA
         maxiters = length(data)
     end

lib/OptimizationManopt/test/runtests.jl

@@ -16,7 +16,7 @@ end
 
 R2 = Euclidean(2)
 
-@testset "Gradient descent" begin
+@testset "Error on no or mismatching manifolds" begin
     x0 = zeros(2)
     p = [1.0, 100.0] 
 
@@ -26,11 +26,28 @@ R2 = Euclidean(2)
 
     optprob_forwarddiff = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
     prob_forwarddiff = OptimizationProblem(optprob_forwarddiff, x0, p)
+    @test_throws ArgumentError("Either manifold not specified in the problem `OptimizationProblem(f, x, p; manifold = SymmetricPositiveDefinite(5))` or it doesn't match the manifold specified in the optimizer `$(opt.M)`") Optimization.solve(prob_forwarddiff, opt)
+
+    optprob = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
+    prob = OptimizationProblem(optprob, x0, p; manifold = SymmetricPositiveDefinite(5))
+    @test_throws ArgumentError("Either manifold not specified in the problem `OptimizationProblem(f, x, p; manifold = SymmetricPositiveDefinite(5))` or it doesn't match the manifold specified in the optimizer `$(opt.M)`") Optimization.solve(prob, opt)
+end
+
+@testset "Gradient descent" begin
+    x0 = zeros(2)
+    p = [1.0, 100.0] 
+
+    stepsize = Manopt.ArmijoLinesearch(R2)
+    opt = OptimizationManopt.GradientDescentOptimizer(R2,
+                                                      stepsize = stepsize)
+
+    optprob_forwarddiff = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
+    prob_forwarddiff = OptimizationProblem(optprob_forwarddiff, x0, p; manifold = R2)
     sol = Optimization.solve(prob_forwarddiff, opt)
     @test sol.minimum < 0.2
 
     optprob_grad = OptimizationFunction(rosenbrock; grad = rosenbrock_grad!)
-    prob_grad = OptimizationProblem(optprob_grad, x0, p)
+    prob_grad = OptimizationProblem(optprob_grad, x0, p; manifold = R2)
     sol = Optimization.solve(prob_grad, opt)
     @test sol.minimum < 0.2
 end
@@ -42,7 +59,7 @@ end
     opt = OptimizationManopt.NelderMeadOptimizer(R2)
 
     optprob = OptimizationFunction(rosenbrock)
-    prob = OptimizationProblem(optprob, x0, p)
+    prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
     sol = Optimization.solve(prob, opt)
     @test sol.minimum < 1e-6
@@ -57,7 +74,7 @@ end
                                                                stepsize = stepsize)
 
     optprob = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
-    prob = OptimizationProblem(optprob, x0, p)
+    prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
     sol = Optimization.solve(prob, opt)
     @test sol.minimum < 0.5
@@ -70,7 +87,7 @@ end
     opt = OptimizationManopt.QuasiNewtonOptimizer(R2)
 
     optprob = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
-    prob = OptimizationProblem(optprob, x0, p)
+    prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
     sol = Optimization.solve(prob, opt)
     @test sol.minimum < 1e-14
@@ -83,7 +100,7 @@ end
     opt = OptimizationManopt.ParticleSwarmOptimizer(R2)
 
     optprob = OptimizationFunction(rosenbrock)
-    prob = OptimizationProblem(optprob, x0, p)
+    prob = OptimizationProblem(optprob, x0, p; manifold = R2)
 
     sol = Optimization.solve(prob, opt)
     @test sol.minimum < 0.1
@@ -112,7 +129,7 @@ end
     f(x, p = nothing) = sum(distance(M, x, data2[i])^2 for i in 1:m)
 
     optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
-    prob = OptimizationProblem(optf, data2[1])
+    prob = OptimizationProblem(optf, data2[1]; manifold = M)
 
     opt = OptimizationManopt.GradientDescentOptimizer(M)
     @time sol = Optimization.solve(prob, opt)