Merge pull request #264 from SciML/optimization

Rename to Optimization.jl

.github/workflows/CI.yml

@@ -15,22 +15,22 @@ jobs:
       matrix:
         group:
           - Core
-          - GalacticBBO
-          - GalacticCMAEvolutionStrategy
-          - GalacticEvolutionary
-          - GalacticFlux
-          - GalacticGCMAES
-          - GalacticMetaheuristics
-          - GalacticMOI
-          - GalacticMultistartOptimization
-          - GalacticNLopt
-          #- GalacticNonconvex
-          - GalacticNOMAD
-          - GalacticOptimJL
-          - GalacticOptimisers
-          - GalacticQuadDIRECT
-          - GalacticSpeedMapping
-          - GalacticPolyalgorithms
+          - OptimizationBBO
+          - OptimizationCMAEvolutionStrategy
+          - OptimizationEvolutionary
+          - OptimizationFlux
+          - OptimizationGCMAES
+          - OptimizationMetaheuristics
+          - OptimizationMOI
+          - OptimizationMultistartOptimization
+          - OptimizationNLopt
+          #- OptimizationNonconvex
+          - OptimizationNOMAD
+          - OptimizationOptimJL
+          - OptimizationOptimisers
+          - OptimizationQuadDIRECT
+          - OptimizationSpeedMapping
+          - OptimizationPolyalgorithms
         version:
           - '1'
           - '1.6'

Project.toml

@@ -1,6 +1,5 @@
-name = "GalacticOptim"
-uuid = "a75be94c-b780-496d-a8a9-0878b188d577"
-authors = ["Vaibhavdixit02 <[email protected]>"]
+name = "Optimization"
+uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 version = "3.5.0"
 
 [deps]

README.md

@@ -1,65 +1,65 @@
-# GalacticOptim.jl
+# Optimization.jl
 
-[![Build Status](https://github.com/SciML/GalacticOptim.jl/workflows/CI/badge.svg)](https://github.com/SciML/GalacticOptim.jl/actions?query=workflow%3ACI)
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](http://galacticoptim.sciml.ai/stable/)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](http://galacticoptim.sciml.ai/dev/)
+[![Build Status](https://github.com/SciML/Optimization.jl/workflows/CI/badge.svg)](https://github.com/SciML/Optimization.jl/actions?query=workflow%3ACI)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](http://Optimization.sciml.ai/stable/)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](http://Optimization.sciml.ai/dev/)
 
-GalacticOptim.jl is a package with a scope that is beyond your normal global optimization
-package. GalacticOptim.jl seeks to bring together all of the optimization packages
+Optimization.jl is a package with a scope that is beyond your normal global optimization
+package. Optimization.jl seeks to bring together all of the optimization packages
 it can find, local and global, into one unified Julia interface. This means, you
-learn one package and you learn them all! GalacticOptim.jl adds a few high-level
+learn one package and you learn them all! Optimization.jl adds a few high-level
 features, such as integrating with automatic differentiation, to make its usage
 fairly simple for most cases, while allowing all of the options in a single
 unified interface.
 
 ## Installation
 
 Assuming that you already have Julia correctly installed, it suffices to import
-GalacticOptim.jl in the standard way:
+Optimization.jl in the standard way:
 
 ```julia
-import Pkg; Pkg.add("GalacticOptim")
+import Pkg; Pkg.add("Optimization")
 ```
-The packages relevant to the core functionality of GalacticOptim.jl will be imported
+The packages relevant to the core functionality of Optimization.jl will be imported
 accordingly and, in most cases, you do not have to worry about the manual
 installation of dependencies. Below is the list of packages that need to be
 installed explicitly if you intend to use the specific optimization algorithms
 offered by them:
 
-- GalacticBBO for [BlackBoxOptim.jl](https://github.com/robertfeldt/BlackBoxOptim.jl)
-- GalacticEvolutionary for [Evolutionary.jl](https://github.com/wildart/Evolutionary.jl) (see also [this documentation](https://wildart.github.io/Evolutionary.jl/dev/))
-- GalacticGCMAES for [GCMAES.jl](https://github.com/AStupidBear/GCMAES.jl)
-- GalacticMOI for [MathOptInterface.jl](https://github.com/jump-dev/MathOptInterface.jl) (usage of algorithm via MathOptInterface API; see also the API [documentation](https://jump.dev/MathOptInterface.jl/stable/))
-- GalacticMetaheuristics for [Metaheuristics.jl](https://github.com/jmejia8/Metaheuristics.jl) (see also [this documentation](https://jmejia8.github.io/Metaheuristics.jl/stable/))
-- GalacticMultistartOptimization for [MultistartOptimization.jl](https://github.com/tpapp/MultistartOptimization.jl) (see also [this documentation](https://juliahub.com/docs/MultistartOptimization/cVZvi/0.1.0/))
-- GalacticNLopt for [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl) (usage via the NLopt API; see also the available [algorithms](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/))
-- GalacticNOMAD for [NOMAD.jl](https://github.com/bbopt/NOMAD.jl) (see also [this documentation](https://bbopt.github.io/NOMAD.jl/stable/))
-- GalacticNonconvex for [Nonconvex.jl](https://github.com/JuliaNonconvex/Nonconvex.jl) (see also [this documentation](https://julianonconvex.github.io/Nonconvex.jl/stable/))
-- GalacticQuadDIRECT for [QuadDIRECT.jl](https://github.com/timholy/QuadDIRECT.jl)
-- GalacticSpeedMapping for [SpeedMapping.jl](https://github.com/NicolasL-S/SpeedMapping.jl) (see also [this documentation](https://nicolasl-s.github.io/SpeedMapping.jl/stable/))
+- OptimizationBBO for [BlackBoxOptim.jl](https://github.com/robertfeldt/BlackBoxOptim.jl)
+- OptimizationEvolutionary for [Evolutionary.jl](https://github.com/wildart/Evolutionary.jl) (see also [this documentation](https://wildart.github.io/Evolutionary.jl/dev/))
+- OptimizationGCMAES for [GCMAES.jl](https://github.com/AStupidBear/GCMAES.jl)
+- OptimizationMOI for [MathOptInterface.jl](https://github.com/jump-dev/MathOptInterface.jl) (usage of algorithm via MathOptInterface API; see also the API [documentation](https://jump.dev/MathOptInterface.jl/stable/))
+- OptimizationMetaheuristics for [Metaheuristics.jl](https://github.com/jmejia8/Metaheuristics.jl) (see also [this documentation](https://jmejia8.github.io/Metaheuristics.jl/stable/))
+- OptimizationMultistartOptimization for [MultistartOptimization.jl](https://github.com/tpapp/MultistartOptimization.jl) (see also [this documentation](https://juliahub.com/docs/MultistartOptimization/cVZvi/0.1.0/))
+- OptimizationNLopt for [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl) (usage via the NLopt API; see also the available [algorithms](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/))
+- OptimizationNOMAD for [NOMAD.jl](https://github.com/bbopt/NOMAD.jl) (see also [this documentation](https://bbopt.github.io/NOMAD.jl/stable/))
+- OptimizationNonconvex for [Nonconvex.jl](https://github.com/JuliaNonconvex/Nonconvex.jl) (see also [this documentation](https://julianonconvex.github.io/Nonconvex.jl/stable/))
+- OptimizationQuadDIRECT for [QuadDIRECT.jl](https://github.com/timholy/QuadDIRECT.jl)
+- OptimizationSpeedMapping for [SpeedMapping.jl](https://github.com/NicolasL-S/SpeedMapping.jl) (see also [this documentation](https://nicolasl-s.github.io/SpeedMapping.jl/stable/))
 
 ## Tutorials and Documentation
 
 For information on using the package,
-[see the stable documentation](https://galacticoptim.sciml.ai/stable/). Use the
-[in-development documentation](https://galacticoptim.sciml.ai/dev/) for the version of
+[see the stable documentation](https://Optimization.sciml.ai/stable/). Use the
+[in-development documentation](https://Optimization.sciml.ai/dev/) for the version of
 the documentation, which contains the unreleased features.
 
 ## Examples
 
 ```julia
-using GalacticOptim
+using Optimization
 rosenbrock(x,p) =  (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
 p  = [1.0,100.0]
 
 prob = OptimizationProblem(rosenbrock,x0,p)
 
-using GalacticOptimJL
+using OptimizationOptimJL
 sol = solve(prob,NelderMead())
 
 
-using GalacticBBO
+using OptimizationBBO
 prob = OptimizationProblem(rosenbrock, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob,BBO_adaptive_de_rand_1_bin_radiuslimited())
 ```
@@ -99,7 +99,7 @@ We can also explore other methods in a similar way:
 
 ```julia
 using ForwardDiff
-f = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff())
+f = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
 prob = OptimizationProblem(f, x0, p)
 sol = solve(prob,BFGS())
 ```
@@ -132,6 +132,6 @@ For instance, the above optimization task produces the following output:
 prob = OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, Fminbox(GradientDescent()))
 ```
-The examples clearly demonstrate that GalacticOptim.jl provides an intuitive
+The examples clearly demonstrate that Optimization.jl provides an intuitive
 way of specifying optimization tasks and offers a relatively
 easy access to a wide range of optimization algorithms.

docs/make.jl

@@ -1,20 +1,20 @@
-using Documenter, GalacticOptim
+using Documenter, Optimization
 using FiniteDiff, ForwardDiff, ModelingToolkit, ReverseDiff, Tracker, Zygote
 
 makedocs(
-    sitename="GalacticOptim.jl",
+    sitename="Optimization.jl",
     authors="Chris Rackauckas, Vaibhav Kumar Dixit et al.",
     clean=true,
     doctest=false,
-    modules=[GalacticOptim, GalacticOptim.SciMLBase, FiniteDiff, 
+    modules=[Optimization, Optimization.SciMLBase, FiniteDiff, 
              ForwardDiff, ModelingToolkit, ReverseDiff, Tracker, Zygote],
 
     format=Documenter.HTML(analytics = "UA-90474609-3",
                            assets=["assets/favicon.ico"],
-                           canonical="https://galacticoptim.sciml.ai/stable/"),
+                           canonical="https://Optimization.sciml.ai/stable/"),
 
     pages=[
-        "GalacticOptim.jl: Unified Global Optimization Package" => "index.md",
+        "Optimization.jl: A Unified Optimization Package" => "index.md",
 
         "Tutorials" => [
             "Basic usage" => "tutorials/intro.md",
@@ -48,6 +48,6 @@ makedocs(
 )
 
 deploydocs(
-    repo="github.com/SciML/GalacticOptim.jl";
+    repo="github.com/SciML/Optimization.jl";
     push_preview=true
 )

docs/src/API/modelingtoolkit.md

@@ -1,6 +1,6 @@
 # ModelingToolkit Integration
 
-GalacticOptim.jl is heavily integrated with the ModelingToolkit.jl
+Optimization.jl is heavily integrated with the ModelingToolkit.jl
 symbolic system for symbolic-numeric optimizations. It provides a
 front-end for automating the construction, parallelization, and
 optimization of code. Optimizers can better interface with the extra
@@ -13,7 +13,7 @@ the [OptimizationFunction documentation](@id optfunction) for more
 details.
 
 Secondly, one can generate `OptimizationProblem`s for use in
-GalacticOptim.jl from purely a symbolic front-end. This is the form
+Optimization.jl from purely a symbolic front-end. This is the form
 users will encounter when using ModelingToolkit.jl directly, and its
 also the form supplied by domain-specific languages. For more information,
 see the [OptimizationSystem documentation](https://mtk.sciml.ai/dev/systems/OptimizationSystem/).

docs/src/index.md

@@ -1,22 +1,22 @@
-# GalacticOptim.jl
+# Optimization.jl
 
-GalacticOptim.jl is a package with a scope that is beyond your normal global optimization
-package. GalacticOptim.jl seeks to bring together all of the optimization packages
+Optimization.jl is a package with a scope that is beyond your normal global optimization
+package. Optimization.jl seeks to bring together all of the optimization packages
 it can find, local and global, into one unified Julia interface. This means, you
-learn one package and you learn them all! GalacticOptim.jl adds a few high-level
+learn one package and you learn them all! Optimization.jl adds a few high-level
 features, such as integrating with automatic differentiation, to make its usage
 fairly simple for most cases, while allowing all of the options in a single
 unified interface.
 
 ## Installation
 
 Assuming that you already have Julia correctly installed, it suffices to import
-GalacticOptim.jl in the standard way:
+Optimization.jl in the standard way:
 
 ```julia
-import Pkg; Pkg.add("GalacticOptim")
+import Pkg; Pkg.add("Optimization")
 ```
-The packages relevant to the core functionality of GalacticOptim.jl will be imported
+The packages relevant to the core functionality of Optimization.jl will be imported
 accordingly and, in most cases, you do not have to worry about the manual
 installation of dependencies. However, you will need to add the specific optimizer
 packages.
@@ -41,6 +41,6 @@ packages.
 
 ✅ = supported
 
-🟡 = supported in downstream library but not yet implemented in `GalacticOptim`; PR to add this functionality are welcome
+🟡 = supported in downstream library but not yet implemented in `Optimization`; PR to add this functionality are welcome
 
 ❌ = not supported

docs/src/optimization_packages/blackboxoptim.md

@@ -1,12 +1,12 @@
 # BlackBoxOptim.jl
 [`BlackBoxOptim`](https://github.com/robertfeldt/BlackBoxOptim.jl) is a is a Julia package implementing **(Meta-)heuristic/stochastic algorithms** that do not require for the optimized function to be differentiable.
 
-## Installation: GalacticBBO.jl
+## Installation: OptimizationBBO.jl
 
-To use this package, install the GalacticBBO package:
+To use this package, install the OptimizationBBO package:
 
 ```julia
-import Pkg; Pkg.add("GalacticBBO")
+import Pkg; Pkg.add("OptimizationBBO")
 ```
 
 ## Global Optimizers
@@ -53,7 +53,7 @@ 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)
-prob = GalacticOptim.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
+prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited(), maxiters=100000, maxtime=1000.0)
 ```
 

docs/src/optimization_packages/cmaevolutionstrategy.md

@@ -3,12 +3,12 @@
 
 The CMAEvolutionStrategy algorithm is called by `CMAEvolutionStrategyOpt()`
 
-## Installation: GalacticCMAEvolutionStrategy.jl
+## Installation: OptimizationCMAEvolutionStrategy.jl
 
-To use this package, install the GalacticCMAEvolutionStrategy package:
+To use this package, install the OptimizationCMAEvolutionStrategy package:
 
 ```julia
-import Pkg; Pkg.add("GalacticCMAEvolutionStrategy")
+import Pkg; Pkg.add("OptimizationCMAEvolutionStrategy")
 ```
 
 ## Global Optimizer
@@ -26,6 +26,6 @@ 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)
-prob = GalacticOptim.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
+prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, CMAEvolutionStrategyOpt())
 ```

docs/src/optimization_packages/evolutionary.md

@@ -1,12 +1,12 @@
 # Evolutionary.jl
 [`Evolutionary`](https://github.com/wildart/Evolutionary.jl) is a Julia package implementing various evolutionary and genetic algorithm.
 
-## Installation: GalacticCMAEvolutionStrategy.jl
+## Installation: OptimizationCMAEvolutionStrategy.jl
 
-To use this package, install the GalacticCMAEvolutionStrategy package:
+To use this package, install the OptimizationCMAEvolutionStrategy package:
 
 ```julia
-import Pkg; Pkg.add("GalacticCMAEvolutionStrategy")
+import Pkg; Pkg.add("OptimizationCMAEvolutionStrategy")
 ```
 
 ## Global Optimizer
@@ -36,6 +36,6 @@ 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)
-prob = GalacticOptim.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
+prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, Evolutionary.CMAES(μ =40 , λ = 100))
 ```

docs/src/optimization_packages/flux.md

@@ -1,11 +1,11 @@
 # Flux.jl
 
-## Installation: GalacticFlux.jl
+## Installation: OptimizationFlux.jl
 
-To use this package, install the GalacticFlux package:
+To use this package, install the OptimizationFlux package:
 
 ```julia
-import Pkg; Pkg.add("GalacticFlux")
+import Pkg; Pkg.add("OptimizationFlux")
 ```
 
 ## Local Unconstrained Optimizers 

docs/src/optimization_packages/gcmaes.md

@@ -1,12 +1,12 @@
 # GCMAES.jl
 [`GCMAES`](https://github.com/AStupidBear/GCMAES.jl) is a Julia package implementing the **Gradient-based Covariance Matrix Adaptation Evolutionary Strategy** which can utilize the gradient information to speed up the optimization process.
 
-## Installation: GalacticGCMAES.jl
+## Installation: OptimizationGCMAES.jl
 
-To use this package, install the GalacticGCMAES package:
+To use this package, install the OptimizationGCMAES package:
 
 ```julia
-import Pkg; Pkg.add("GalacticGCMAES")
+import Pkg; Pkg.add("OptimizationGCMAES")
 ```
 
 ## Global Optimizer
@@ -26,7 +26,7 @@ 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)
-prob = GalacticOptim.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
+prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, GCMAESOpt())
 ```
 
@@ -36,7 +36,7 @@ We can also utilise the gradient information of the optimization problem to aid
 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.ForwardDiff)
-prob = GalacticOptim.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
+f = OptimizationFunction(rosenbrock, Optimization.ForwardDiff)
+prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0,-1.0], ub = [1.0,1.0])
 sol = solve(prob, GCMAESOpt())
 ```