documentation page added

.github/workflows/docs.yml

@@ -0,0 +1,24 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - master # update to match your development branch (master, main, dev, trunk, ...)
+    tags: '*'
+  pull_request:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1.9'
+      - name: Install dependencies
+        run: julia --project=docs/ docs/install.jl
+      - name: Build and deploy
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key
+        run: julia --project=docs/ docs/make.jl

README.md

@@ -3,7 +3,7 @@
 
 | **License** | **Documentation** | **Build Status** | **Coverage** | **Citation** |
 |:-----------------:|:----------------:|:----------------:|:----------------:|:----------------:|
-| [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | [![doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://sshin23.github.io/SIMDiff.jl/dev)  | [![build](https://github.com/sshin23/SIMDiff.jl/actions/workflows/test.yml/badge.svg)](https://github.com/sshin23/SIMDiff.jl/actions/workflows/test.yml) | [![codecov](https://codecov.io/gh/sshin23/SIMDiff.jl/branch/main/graph/badge.svg?token=8ViJWBWnZt)](https://codecov.io/gh/sshin23/SIMDiff.jl) |
+| [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | [![doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://github.com/sshin23/SIMDiff.jl/dev)  | [![build](https://github.com/sshin23/SIMDiff.jl/actions/workflows/test.yml/badge.svg)](https://github.com/sshin23/SIMDiff.jl/actions/workflows/test.yml) | [![codecov](https://codecov.io/gh/sshin23/SIMDiff.jl/branch/main/graph/badge.svg?token=8ViJWBWnZt)](https://codecov.io/gh/sshin23/SIMDiff.jl) |
 
 ## Introduction
 SIMDiff.jl employs what we call **SIMD abstraction for nonlinear programs** (NLPs), which allows for the **preservation of the parallelizable structure** within the model equations, facilitating **efficient, parallel derivative evaluations** on the **GPU**.

docs/make.jl

@@ -1,20 +1,14 @@
-using Documenter, MadDiff, Literate
+using Documenter, SIMDiff, Literate
 
 const _PAGES = [
     "Introduction" => "index.md",
     "Quick Start"=>"guide.md",
-    "How it Works" => "tutorial.md",
-    "API Manual" => [
-        "MadDiffCore" => "core.md",
-        "MadDiffSpecialFunctions" => "special.md",
-        "MadDiffModels" => "models.md",
-        "MadDiffMOI" => "moi.md",
-    ]
+    "API Manual" => "core.md",
 ]
 
 const _JL_FILENAMES = [
     "guide.jl",
-    "tutorial.jl"
+    # "tutorial.jl"
 ]
 
 for jl_filename in _JL_FILENAMES
@@ -30,15 +24,15 @@ end
 
 
 makedocs(
-    sitename = "MadDiff",
+    sitename = "SIMDiff.jl",
     authors = "Sungho Shin",
     format = Documenter.LaTeX(platform="docker"),
     pages = _PAGES
 )
 
 makedocs(
-    sitename = "MadDiff",
-    modules = [MadDiff],
+    sitename = "SIMDiff.jl",
+    modules = [SIMDiff],
     authors = "Sungho Shin",
     format = Documenter.HTML(
         prettyurls = get(ENV, "CI", nothing) == "true",
@@ -51,6 +45,6 @@ makedocs(
 
 
 deploydocs(
-    repo = "github.com/sshin23/MadDiff.jl.git"
+    repo = "github.com/sshin23/SIMDiff.jl.git"
 )
 

docs/src/core.md

@@ -1,4 +1,4 @@
-# MadDiffCore
+# SIMDiff
 ```@autodocs
-Modules = [MadDiffCore]
+Modules = [SIMDiff]
 ```

docs/src/guide.jl

@@ -1,46 +1,41 @@
 # # Getting Started
-# SIMDiff provides a built-in API for creating nonlinear prgogramming models and allows solving the created models using NLP solvers (in particular, those that are interfaced with `NLPModels`, such as [NLPModelsIpopt](https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl). We now use `SIMDiff`'s bulit-in API to model the following nonlinear program:
+# SIMDiff can create nonlinear prgogramming models and allows solving the created models using NLP solvers (in particular, those that are interfaced with `NLPModels`, such as [NLPModelsIpopt](https://github.com/JuliaSmoothOptimizers/NLPModelsIpopt.jl). We now use `SIMDiff` to model the following nonlinear program:
 # ```math
 # \begin{aligned}
 # \min_{\{x_i\}_{i=0}^N} &\sum_{i=2}^N  100(x_{i-1}^2-x_i)^2+(x_{i-1}-1)^2\\
 # \text{s.t.} &  3x_{i+1}^3+2x_{i+2}-5+\sin(x_{i+1}-x_{i+2})\sin(x_{i+1}+x_{i+2})+4x_{i+1}-x_i e^{x_i-x_{i+1}}-3 = 0
 # \end{aligned}
 # ```
 # We model the problem with:
+using SIMDiff
+
+# We set
 N = 10000
 
-# First, we create a `SIMDiffModel`.
-m = SIMDiffModel() 
+# First, we create a `SIMDiff.Core`.
+c = SIMDiff.Core()
 
 # The variables can be created as follows:
-x = [variable(m; start = mod(i,2)==1 ? -1.2 : 1.) for i=1:N];
+
+x = SIMDiff.variable(
+    c, N;
+    start = (mod(i,2)==1 ? -1.2 : 1. for i=1:N)
+)
 
 # The objective can be set as follows:
-objective(m, sum(100(x[i-1]^2-x[i])^2+(x[i-1]-1)^2 for i=2:N));
+SIMDiff.objective(c, 100*(x[i-1]^2-x[i])^2+(x[i-1]-1)^2 for i in 2:N)
 
 # The constraints can be set as follows:
-for i=1:N-2
-    constraint(m, 3x[i+1]^3+2*x[i+2]-5+sin(x[i+1]-x[i+2])sin(x[i+1]+x[i+2])+4x[i+1]-x[i]exp(x[i]-x[i+1])-3 == 0);
-end
+SIMDiff.constraint(
+    c,
+    3x[i+1]^3+2*x[i+2]-5+sin(x[i+1]-x[i+2])sin(x[i+1]+x[i+2])+4x[i+1]-x[i]exp(x[i]-x[i+1])-3
+    for i in 1:N-2)
 
-# The important last step is instantiating the model. This step must be taken before calling optimizers.
-instantiate!(m)
+# Finally, we create an NLPModel.
+m = SIMDiff.Model(c)
 
 # To solve the problem with `Ipopt`,
 using NLPModelsIpopt
 sol = ipopt(m);
 
 # The solution `sol` contains the field `sol.solution` holding the optimized parameters.
-
-# ### SIMDiff as an AD backend of JuMP
-# SIMDiff can be used as an automatic differentiation backend of JuMP. The problem above can be modeled in `JuMP` and solved with `Ipopt` along with `SIMDiff`
-
-using JuMP, Ipopt
-
-m = JuMP.Model(Ipopt.Optimizer) 
-
-@variable(m, x[i=1:N], start=mod(i,2)==1 ? -1.2 : 1.)
-@NLobjective(m, Min, sum(100(x[i-1]^2-x[i])^2+(x[i-1]-1)^2 for i=2:N))
-@NLconstraint(m, [i=1:N-2], 3x[i+1]^3+2*x[i+2]-5+sin(x[i+1]-x[i+2])sin(x[i+1]+x[i+2])+4x[i+1]-x[i]exp(x[i]-x[i+1])-3 == 0)
-
-optimize!(m; differentiation_backend = SIMDiffAD())