Reexport

Author: Vaibhavdixit02

lib/OptimizationManopt/Project.toml

@@ -8,4 +8,5 @@ ManifoldDiff = "af67fdf4-a580-4b9f-bbec-742ef357defd"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
 ManifoldsBase = "3362f125-f0bb-47a3-aa74-596ffd7ef2fb"
 Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
-Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

lib/OptimizationManopt/src/OptimizationManopt.jl

@@ -1,5 +1,7 @@
 module OptimizationManopt
 
+using Reexport
+@reexport using Manopt
 using Optimization, Manopt, ManifoldsBase, ManifoldDiff
 
 """
@@ -228,9 +230,9 @@ end
 
 ## Optimization.jl stuff
 
-function build_loss(f::OptimizationFunction, prob, cur)
+function build_loss(f::OptimizationFunction, prob)
     function (::AbstractManifold, θ)
-        x = f.f(θ, prob.p, cur...)
+        x = f.f(θ)
         __x = first(x)
         return prob.sense === Optimization.MaxSense ? -__x : __x
     end
@@ -287,7 +289,7 @@ function SciMLBase.__solve(prob::OptimizationProblem,
 
     f = Optimization.instantiate_function(prob.f, prob.u0, prob.f.adtype, prob.p)
 
-    _loss = build_loss(f, prob, cur)
+    _loss = build_loss(f, prob)
 
     gradF = build_gradF(f, prob, cur)
 

lib/OptimizationManopt/test/runtests.jl

@@ -131,10 +131,10 @@ 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]; manifold = M)
+    prob = OptimizationProblem(optf, data2[1]; manifold = M, maxiters = 1000)
 
     opt = OptimizationManopt.GradientDescentOptimizer(M)
     @time sol = Optimization.solve(prob, opt)
 
-    @test sol.u ≈ q
+    @test sol.u ≈ q atol = 1e-2
 end