Fix dependency structures

Author: ChrisRackauckas

lib/OptimizationAuglag/test/runtests.jl

@@ -4,7 +4,7 @@ using OptimizationOptimisers
 using OptimizationAuglag
 using ForwardDiff
 using OptimizationBase: OptimizationCache
-using OptimizationBase.SciMLBase: OptimizationFunction
+using SciMLBase: OptimizationFunction
 using Test
 
 @testset "OptimizationAuglag.jl" begin

lib/OptimizationBBO/test/runtests.jl

@@ -1,5 +1,5 @@
 using OptimizationBBO, Optimization, BlackBoxOptim
-using Optimization.SciMLBase: MultiObjectiveOptimizationFunction
+using SciMLBase: MultiObjectiveOptimizationFunction
 using Test
 
 @testset "OptimizationBBO.jl" begin
@@ -9,14 +9,14 @@ using Test
     l1 = rosenbrock(x0, _p)
 
     optprob = OptimizationFunction(rosenbrock)
-    prob = Optimization.OptimizationProblem(optprob, x0, _p, lb = [-1.0, -1.0],
+    prob = OptimizationBase.OptimizationProblem(optprob, x0, _p, lb = [-1.0, -1.0],
         ub = [0.8, 0.8])
     sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited())
     @test 10 * sol.objective < l1
 
     @test (@allocated solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited())) < 1e7
 
-    prob = Optimization.OptimizationProblem(optprob, nothing, _p, lb = [-1.0, -1.0],
+    prob = OptimizationBase.OptimizationProblem(optprob, nothing, _p, lb = [-1.0, -1.0],
         ub = [0.8, 0.8])
     sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited())
     @test 10 * sol.objective < l1
@@ -74,7 +74,7 @@ using Test
             end
 
             mof_1 = MultiObjectiveOptimizationFunction(multi_obj_func_1)
-            prob_1 = Optimization.OptimizationProblem(mof_1, u0; lb = lb, ub = ub)
+            prob_1 = OptimizationBase.OptimizationProblem(mof_1, u0; lb = lb, ub = ub)
             sol_1 = solve(prob_1, opt, NumDimensions = 2,
                 FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
 
@@ -101,7 +101,7 @@ using Test
             end
 
             mof_1 = MultiObjectiveOptimizationFunction(multi_obj_func_1)
-            prob_1 = Optimization.OptimizationProblem(mof_1, u0; lb = lb, ub = ub)
+            prob_1 = OptimizationBase.OptimizationProblem(mof_1, u0; lb = lb, ub = ub)
             sol_1 = solve(prob_1, opt, NumDimensions = 2,
                 FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true),
                 callback = cb)
@@ -127,7 +127,7 @@ using Test
             end
 
             mof_2 = MultiObjectiveOptimizationFunction(multi_obj_func_2)
-            prob_2 = Optimization.OptimizationProblem(mof_2, u0; lb = lb, ub = ub)
+            prob_2 = OptimizationBase.OptimizationProblem(mof_2, u0; lb = lb, ub = ub)
             sol_2 = solve(prob_2, opt, NumDimensions = 2,
                 FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
 
@@ -147,7 +147,7 @@ using Test
             end
 
             mof_3 = MultiObjectiveOptimizationFunction(multi_obj_func_3)
-            prob_3 = Optimization.OptimizationProblem(mof_3, u0; lb = lb, ub = ub)
+            prob_3 = OptimizationBase.OptimizationProblem(mof_3, u0; lb = lb, ub = ub)
             sol_3 = solve(prob_3, opt, NumDimensions = 2,
                 FitnessScheme = ParetoFitnessScheme{2}(is_minimizing = true))
 

lib/OptimizationBase/ext/OptimizationEnzymeExt.jl

@@ -1,8 +1,8 @@
 module OptimizationEnzymeExt
 
 import OptimizationBase, OptimizationBase.ArrayInterface
-import OptimizationBase.SciMLBase: OptimizationFunction
-import OptimizationBase.SciMLBase
+import SciMLBase: OptimizationFunction
+import SciMLBase
 import OptimizationBase.LinearAlgebra: I, dot
 import OptimizationBase.ADTypes: AutoEnzyme
 using Enzyme

lib/OptimizationBase/ext/OptimizationMTKExt.jl

@@ -1,8 +1,8 @@
 module OptimizationMTKExt
 
 import OptimizationBase, OptimizationBase.ArrayInterface
-import OptimizationBase.SciMLBase
-import OptimizationBase.SciMLBase: OptimizationFunction
+import SciMLBase
+import SciMLBase: OptimizationFunction
 import OptimizationBase.ADTypes: AutoModelingToolkit, AutoSymbolics, AutoSparse
 using ModelingToolkit
 

lib/OptimizationBase/ext/OptimizationZygoteExt.jl

@@ -3,7 +3,7 @@ module OptimizationZygoteExt
 using OptimizationBase, SparseArrays
 using OptimizationBase.FastClosures
 import OptimizationBase.ArrayInterface
-import OptimizationBase.SciMLBase: OptimizationFunction
+import SciMLBase: OptimizationFunction
 import OptimizationBase.LinearAlgebra: I, dot
 import DifferentiationInterface
 import DifferentiationInterface: prepare_gradient, prepare_hessian, prepare_hvp,

lib/OptimizationBase/src/OptimizationDIExt.jl

@@ -1,6 +1,6 @@
 using OptimizationBase
 import OptimizationBase.ArrayInterface
-import OptimizationBase.SciMLBase: OptimizationFunction
+import SciMLBase: OptimizationFunction
 import OptimizationBase.LinearAlgebra: I
 import DifferentiationInterface
 import DifferentiationInterface: prepare_gradient, prepare_hessian, prepare_hvp,

lib/OptimizationBase/src/OptimizationDISparseExt.jl

@@ -1,6 +1,6 @@
 using OptimizationBase
 import OptimizationBase.ArrayInterface
-import OptimizationBase.SciMLBase: OptimizationFunction
+import SciMLBase: OptimizationFunction
 import OptimizationBase.LinearAlgebra: I
 import DifferentiationInterface
 import DifferentiationInterface: prepare_gradient, prepare_hessian, prepare_hvp,

lib/OptimizationBase/src/function.jl

@@ -21,9 +21,9 @@ function rep_pars_vals!(e, p) end
 """
     instantiate_function(f, x, ::AbstractADType, p, num_cons = 0)::OptimizationFunction
 
-This function is used internally by Optimization.jl to construct
+This function is used internally by OptimizationBase.jl to construct
 the necessary extra functions (gradients, Hessians, etc.) before
-optimization. Each of the ADType dispatches use the supplied automatic
+OptimizationBase. Each of the ADType dispatches use the supplied automatic
 differentiation type in order to specify how the construction process
 occurs.
 

lib/OptimizationBase/src/solve.jl

@@ -12,11 +12,11 @@ end
 const OPTIMIZER_MISSING_ERROR_MESSAGE = """
                                         Optimization algorithm not found. Either the chosen algorithm is not a valid solver
                                         choice for the `OptimizationProblem`, or the Optimization solver library is not loaded.
-                                        Make sure that you have loaded an appropriate Optimization.jl solver library, for example,
+                                        Make sure that you have loaded an appropriate OptimizationBase.jl solver library, for example,
                                         `solve(prob,Optim.BFGS())` requires `using OptimizationOptimJL` and
                                         `solve(prob,Adam())` requires `using OptimizationOptimisers`.
 
-                                        For more information, see the Optimization.jl documentation: <https://docs.sciml.ai/Optimization/stable/>.
+                                        For more information, see the OptimizationBase.jl documentation: <https://docs.sciml.ai/Optimization/stable/>.
                                         """
 
 struct OptimizerMissingError <: Exception

lib/OptimizationBase/test/cvxtest.jl

@@ -5,7 +5,7 @@ function f(x, p = nothing)
     return exp(x[1]) + x[1]^2
 end
 
-optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
+optf = OptimizationFunction(f, OptimizationBase.AutoForwardDiff())
 prob = OptimizationProblem(optf, [0.4], structural_analysis = true)
 
 @time sol = solve(prob, OptimizationLBFGSB.LBFGSB(), maxiters = 1000)
@@ -42,7 +42,7 @@ M = SymmetricPositiveDefinite(5)
 data2 = [exp(M, q, σ * rand(M; vector_at = q)) for i in 1:m];
 
 f(x, p = nothing) = sum(SymbolicAnalysis.distance(M, data2[i], x)^2 for i in 1:5)
-optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
+optf = OptimizationFunction(f, OptimizationBase.AutoForwardDiff())
 prob = OptimizationProblem(optf, data2[1]; manifold = M, structural_analysis = true)
 
 opt = OptimizationManopt.GradientDescentOptimizer()