Update for GalacticOptim.jl -> Optimization.jl change

Fixes #1620

Author: ChrisRackauckas

Project.toml

@@ -81,8 +81,8 @@ julia = "1.6"
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"
-GalacticOptimJL = "9d3c5eb1-403b-401b-8c0f-c11105342e6b"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReferenceTests = "324d217c-45ce-50fc-942e-d289b448e8cf"
@@ -93,4 +93,4 @@ Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["BenchmarkTools", "ForwardDiff", "GalacticOptim", "GalacticOptimJL", "OrdinaryDiffEq", "Random", "ReferenceTests", "SafeTestsets", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials"]
+test = ["BenchmarkTools", "ForwardDiff", "Optimization", "OptimizationOptimJL", "OrdinaryDiffEq", "Random", "ReferenceTests", "SafeTestsets", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials"]

docs/Project.toml

@@ -3,7 +3,7 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-GalacticOptim = "a75be94c-b780-496d-a8a9-0878b188d577"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"

docs/src/index.md

@@ -145,7 +145,7 @@ system:
       `SDESystem`, and `JumpSystem`
 - [NonlinearSolve.jl](https://github.com/JuliaComputing/NonlinearSolve.jl)
     - High performance numerical solving of `NonlinearSystem`
-- [GalacticOptim.jl](https://github.com/SciML/GalacticOptim.jl)
+- [Optimization.jl](https://github.com/SciML/Optimization.jl)
     - Multi-package interface for numerical solving `OptimizationSystem`
 - [NeuralPDE.jl](https://github.com/SciML/NeuralPDE.jl)
     - Physics-Informed Neural Network (PINN) training on `PDESystem`

docs/src/tutorials/nonlinear_optimal_control.md

@@ -81,13 +81,13 @@ sys = runge_kutta_discretize(sys,dt,tspan)
 
 Now `sys` is an `OptimizationSystem` which, when solved, gives the values of
 `x(t)`, `v(t)`, and `u(t)`. Thus we solve the `OptimizationSystem` using
-GalacticOptim.jl:
+Optimization.jl:
 
 ```julia
 u0 = rand(length(states(sys))) # guess for the state values
 prob = OptimizationProblem(sys,u0,[0.1,0.1],grad=true)
 
-using GalacticOptim, Optim
+using Optimization, Optim
 sol = solve(prob,BFGS())
 ```
 

docs/src/tutorials/optimization.md

@@ -1,7 +1,7 @@
 # Modeling Optimization Problems
 
 ```julia
-using ModelingToolkit, GalacticOptim, Optim
+using ModelingToolkit, Optimization, OptimizationOptimJL
 
 @variables x y
 @parameters a b

test/controlsystem.jl

@@ -1,4 +1,4 @@
-using ModelingToolkit, GalacticOptim, GalacticOptimJL
+using ModelingToolkit, Optimization, OptimizationOptimJL
 
 @variables t x(t) v(t) u(t)
 @parameters p[1:2]

test/modelingtoolkitize.jl

@@ -1,5 +1,5 @@
 using OrdinaryDiffEq, ModelingToolkit, Test
-using GalacticOptim, RecursiveArrayTools, GalacticOptimJL
+using Optimization, RecursiveArrayTools, OptimizationOptimJL
 
 N = 32
 const xyd_brusselator = range(0, stop = 1, length = N)

test/optimizationsystem.jl

@@ -1,4 +1,4 @@
-using ModelingToolkit, SparseArrays, Test, GalacticOptim, GalacticOptimJL
+using ModelingToolkit, SparseArrays, Test, Optimization, OptimizationOptimJL
 
 @variables x y
 @parameters a b
@@ -51,7 +51,7 @@ rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
 _p = [1.0, 100.0]
 
-f = OptimizationFunction(rosenbrock, GalacticOptim.AutoModelingToolkit())
+f = OptimizationFunction(rosenbrock, Optimization.AutoModelingToolkit())
 prob = OptimizationProblem(f, x0, _p)
 sol = solve(prob, Newton())