add more sub-headers to sidebar

README.md

@@ -31,7 +31,7 @@ The `JuliaNonconvex` organization hosts a number of packages which are available
 
 ## Design philosophy
 
-Nonconvex.jl is a Julia package that implements and wraps a number of constrained nonlinear and mixed integer nonlinear programming solvers. There are 4 unique features of Nonconvex.jl compared to similar packages such as JuMP.jl and NLPModels.jl:
+Nonconvex.jl is a Julia package that implements and wraps a number of constrained nonlinear and mixed integer nonlinear programming solvers. There are 3 features of Nonconvex.jl compared to similar packages such as JuMP.jl and NLPModels.jl:
 
 1. Emphasis on a function-based API. Objectives and constraints are normal Julia functions.
 2. The ability to nest algorithms to create more complicated algorithms.

docs/make.jl

@@ -6,7 +6,16 @@ makedocs(
     pages = [
         "Getting started" => "index.md",
         "Problem definition" => "problem/problem.md",
-        "Gradients, Jacobians and Hessians" => "gradients/gradients.md",
+        "Gradients, Jacobians and Hessians" => [
+            "Overview" => "gradients/gradients.md",
+            "gradients/user_defined.md",
+            "gradients/other_ad.md",
+            "gradients/chainrules_fd.md",
+            "gradients/sparse.md",
+            "gradients/symbolic.md",
+            "gradients/implicit.md",
+            "gradients/history.md",
+        ],
         "Algorithms" => [
             "Overview" => "algorithms/algorithms.md",
             "algorithms/mma.md",

docs/src/gradients/gradients.md

@@ -13,6 +13,8 @@ In some cases, function modifiers can even be composed on top of each other to c
 
 > In `Nonconvex`, function modifiers modify the behaviour of a function when differentiated once or twice using either `ForwardDiff` or any [`ChainRules`](https://github.com/JuliaDiff/ChainRules.jl)-compatible AD package, such as `Zygote.jl`. The following features are all implemented in [`NonconvexUtils.jl`](https://github.com/JuliaNonconvex/NonconvexUtils.jl) and re-exported from `Nonconvex`.
 
+## Table of contents
+
 ```@contents
 Pages = ["user_defined.md", "other_ad.md", "chainrules_fd.md", "sparse.md", "symbolic.md", "implicit.md", "history.md"]
 Depth = 3

docs/src/index.md

@@ -1,6 +1,6 @@
 # Nonconvex.jl Documentation
 
-Nonconvex.jl is a Julia package that implements and wraps a number of constrained nonlinear and mixed integer nonlinear programming solvers. There are 4 unique features of Nonconvex.jl compared to similar packages such as JuMP.jl and NLPModels.jl:
+Nonconvex.jl is a Julia package that implements and wraps a number of constrained nonlinear and mixed integer nonlinear programming solvers. There are 3 features of Nonconvex.jl compared to similar packages such as JuMP.jl and NLPModels.jl:
 
 1. Emphasis on a function-based API. Objectives and constraints are normal Julia functions.
 2. The ability to nest algorithms to create more complicated algorithms.

docs/src/problem/problem.md

@@ -5,6 +5,8 @@ There are 3 ways to define a model in Nonconvex.jl:
 2. `DictModel` which assumes each variable has a name. The decision variables are stored in an `OrderedDict`, an ordered dictionary data structure.
 3. Start from `JuMP.Model` and convert it to `DictModel`. This is convenient to make use of `JuMP`'s user-friendly macros for variable and linear expression, objective or constraint definitions.
 
+## Table of contents
+
 ```@contents
 Pages = ["model.md", "dict_model.md", "queries.md"]
 Depth = 3