Merge pull request #1 from ChrisRackauckas/patch-1

Update README.md

Author: ChrisRackauckas

README.md

@@ -1,3 +1,84 @@
-# GalacticOptim
+# GalacticOptim.jl
 
 [![Build Status](https://travis-ci.com/Vaibhavdixit02/GalacticOptim.jl.svg?branch=master)](https://travis-ci.com/Vaibhavdixit02/GalacticOptim.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
+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
+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.
+
+#### Note: This package is currently in development and is not released. The README is currently a development roadmap.
+
+## Examples
+
+```julia
+using GalacticOptim
+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(f,x0,p)
+sol = solve(prob,BFGS())
+
+prob = OptimizationProblem(f,lower_bounds=[-1.0,-1.0],upper_bounds=[1.0,1.0])
+sol = solve(prob,BFGS())
+
+using BlackBoxOptim
+sol = solve(prob,BBO())
+
+using Flux
+sol = solve(prob,ADAM(0.01),maxiters = 100)
+```
+
+### Automatic Differentiation Choices
+
+While one can fully define all of the derivative functions associated with
+nonlinear constrained optimization directly, in many cases it's easiest to just
+rely on automatic differentiation to derive those functions. In GalacticOptim.jl,
+you can provide as few functions as you want, or give a differentiation library
+choice.
+
+- `AutoForwardDiff()`
+- `AutoReverseDiff(compile=false)`
+- `AutoTracker()`
+- `AutoZygote()`
+- `AutoFiniteDiff()`
+- `AutoModelingToolkit()`
+
+### Symbolic DSL Interface
+
+Provided by ModelingToolkit.jl
+
+### API Documentation
+
+```julia
+OptimizationFunction(f;
+                     grad = AutoForwardDiff(),
+                     hes = AutoForwardDiff(),
+                     eqconstraints = AutoForwardDiff(),
+                     neqconstraints = AutoForwardDiff(),
+                     eqconstraints_jac = AutoForwardDiff(),
+                     neqconstraints_jac = AutoForwardDiff(),
+                     colorvec,hessparsity,eqsparsity,neqsparsity)
+```
+
+```julia
+OptimizationProblem(f,x0=nothing,p=nothing;
+                    lower_bounds=nothing,
+                    upper_bounds=nothing)
+```
+
+```julia
+solve(prob,alg;kwargs...)
+```
+
+Keyword arguments:
+
+  - `maxiters`
+  - `abstol`
+  - `reltol`
+
+Output Struct: