Overhaul the documentation

docs/pages.jl

@@ -1,8 +1,12 @@
 # Put in a separate page so it can be used by SciMLDocs.jl
 
 pages = ["index.md",
-    "Tutorials" => Any["tutorials/nonlinear.md",
-        "tutorials/advanced.md",
+    "tutorials/getting_started.md"
+    "Tutorials" => Any[
+        "tutorials/code_optimization.md",
+        "tutorials/large_systems.md",
+        "tutorials/small_compile.md",
+        "tutorials/termination_conditions.md",
         "tutorials/iterator_interface.md"],
     "Basics" => Any["basics/NonlinearProblem.md",
         "basics/NonlinearFunctions.md",

docs/src/basics/NonlinearProblem.md

@@ -1,4 +1,4 @@
-# Nonlinear Problems
+# [Nonlinear Problems](@id problems)
 
 ## The Three Types of Nonlinear Problems
 

docs/src/basics/NonlinearSolution.md

@@ -1,4 +1,4 @@
-# Nonlinear Solutions
+# [Nonlinear Solutions](@id solution)
 
 ```@docs
 SciMLBase.NonlinearSolution

docs/src/basics/TerminationCondition.md

@@ -1,4 +1,4 @@
-# Termination Conditions
+# [Termination Conditions](@id termination_condition)
 
 Provides a API to specify termination conditions for [`NonlinearProblem`](@ref) and
 [`SteadyStateProblem`](@ref). For details on the various termination modes, i.e.,

docs/src/solvers/BracketingSolvers.md

@@ -1,4 +1,4 @@
-# Interval Rootfinding Methods (Bracketing Solvers)
+# [Interval Rootfinding Methods (Bracketing Solvers)](@id bracketing)
 
 `solve(prob::IntervalNonlinearProblem,alg;kwargs)`
 

docs/src/solvers/SteadyStateSolvers.md

@@ -1,4 +1,4 @@
-# Steady State Solvers
+# [Steady State Solvers](@id ss_solvers)
 
 `solve(prob::SteadyStateProblem,alg;kwargs)`
 

docs/src/tutorials/code_optimization.md

@@ -0,0 +1,31 @@
+# [Code Optimization for Solving Nonlinear Systems](@id code_optimization)
+
+## Optimizing Nonlinear Solver Code for Small Systems
+
+```@example
+using NonlinearSolve, StaticArrays
+
+f(u, p) = u .* u .- p
+u0 = @SVector[1.0, 1.0]
+p = 2.0
+probN = NonlinearProblem(f, u0, p)
+sol = solve(probN, NewtonRaphson(), reltol = 1e-9)
+```
+
+## Using Jacobian Free Newton Krylov (JNFK) Methods
+
+If we want to solve the first example, without constructing the entire Jacobian
+
+```@example
+using NonlinearSolve, LinearSolve
+
+function f!(res, u, p)
+    @. res = u * u - p
+end
+u0 = [1.0, 1.0]
+p = 2.0
+prob = NonlinearProblem(f!, u0, p)
+
+linsolve = LinearSolve.KrylovJL_GMRES()
+sol = solve(prob, NewtonRaphson(; linsolve), reltol = 1e-9)
+```

docs/src/tutorials/getting_started.md

@@ -0,0 +1,148 @@
+# Getting Started with Nonlinear Rootfinding in Julia
+
+NonlinearSolve.jl is a system for solving rootfinding problems, i.e. finding
+the value $$u$$ such that $$f(u) = 0$$. In this tutorial we will go through
+the basics of NonlinearSolve.jl, demonstrating the core ideas and leading you
+to understanding the deeper parts of the documentation.
+
+## The Three Types of Nonlinear Systems
+
+There are three types of nonlinear systems:
+
+1. The "standard nonlinear system", i.e. the `NonlinearProblem`. This is a
+   system of equations with an initial condition where you want to satisfy
+   all equations simultaniously.
+2. The "interval rootfinding problem", i.e. the `IntervalNonlinearProblem`.
+   This is the case where you're given an interval `[a,b]` and need to find
+   where `f(u) = 0` for `u` inside the bounds.
+3. The "steady state problem", i.e. find the `u` such that `u' = f(u) = 0`.
+   While related to (1), it's not entirely the same because there's a uniquely
+   defined privledged root.
+4. The nonlinear least squares problem, which is an overconstrained nonlinear
+   system (i.e. more equations than states) which might not be satisfiable, i.e.
+   there may be no `u` such that `f(u) = 0`, and thus we find the `u` which
+   minimizes `||f(u)||` in the least squares sense.
+
+For now let's focus on the first two. The other two are covered in later tutorials,
+but from the first two we can show the general flow of the NonlinearSolve.jl package.
+
+## Problem Type 1: Solving Nonlinear Systems of Equations
+
+A nonlinear system $$f(u) = 0$$ is specified by defining a function `f(u,p)`,
+where `p` are the parameters of the system. For example, the following solves
+the vector equation $$f(u) = u^2 - p$$ for a vector of equations:
+
+```@example
+using NonlinearSolve
+
+f(u, p) = u .* u .- p
+u0 = [1.0, 1.0]
+p = 2.0
+prob = NonlinearProblem(f, u0, p)
+sol = solve(prob)
+```
+
+where `u0` is the initial condition for the rootfinder. Native NonlinearSolve.jl
+solvers use the given type of `u0` to determine the type used within the solver
+and the return. Note that the parameters `p` can be any type, but most are an
+AbstractArray for automatic differentiation.
+
+### Investigating the Solution
+
+To investigate the solution, one can look at the elements of the `NonlinearSolution`.
+The most important value is `sol.u`: this is the `u` that satisfies `f(u) = 0`. For example:
+
+```@example
+u = sol.u
+```
+
+```@example
+f(u, p)
+```
+
+This final value, the difference of the solution against zero, can also be found with `sol.resid`:
+
+```@example
+sol.resid
+```
+
+To know if the solution converged, or why the solution had not converged we can check the return
+code (`retcode`):
+
+```@example
+sol.retcode
+```
+
+There are multiple return codes which can mean the solve was successful, and thus we can use the
+general command `SciMLBase.successful_retcode` to check whether the solution process exited as
+intended:
+
+```@exmaple
+SciMLBase.successful_retcode(sol)
+```
+
+If we're curious about what it took to solve this equation, then you're in luck because all of the
+details can be found in `sol.stats`:
+
+```@example
+sol.stats
+```
+
+For more information on `NonlinearSolution`s, see the [`NonlinearSolution` manual page](@ref solution).
+
+### Interacting with the Solver Options
+
+While `sol = solve(prob)` worked for our case here, in many situations you may need to interact more
+deeply with how the solving process is done. First things first, you can specify the solver using the
+positional arguments. For example, let's set the solver to `TrustRegion()`:
+
+```@example
+solve(prob, TrustRegion())
+```
+
+For a complete list of solver choices, see [the nonlinear system solvers page](@ref nonlinearsystemsolvers).
+
+Next we can modify the tolerances. Here let's set some really low tolerances to force a tight solution:
+
+```@example
+solve(prob, TrustRegion(), reltol=1e-12, abstol=1e-12)
+```
+
+There are many more options for doing this configuring. Specifically for handling termination conditions,
+see the [Termination Conditions](@ref termination_condition) page for more details. And for more details on
+all of the available keyword arguments, see the [solver options](@ref solver_options) page.
+
+## Problem Type 2: Solving Interval Rootfinding Problems with Bracketing Methods
+
+For scalar rootfinding problems, bracketing methods exist in NonlinearSolve. The difference with bracketing
+methods is that with bracketing methods, instead of giving a `u0` initial condition, you pass a `uspan (a,b)`
+bracket in which the zero is expected to live. For example:
+
+```@example
+using NonlinearSolve
+f(u, p) = u * u - 2.0
+uspan = (1.0, 2.0) # brackets
+prob_int = IntervalNonlinearProblem(f, uspan)
+sol = solve(prob_int)
+```
+
+All of the same option handling from before works just as before, now just with different solver choices
+(see the [bracketing solvers](@ref bracketing) page for more details). For example, let's set the solver
+to `ITP()` and set a high absolute tolerance:
+
+```@example
+sol = solve(prob_int, ITP(), abstol = 0.01)
+```
+
+## Going Beyond the Basics: How to use the Documentation
+
+Congrats, you now know how to use the basics of NonlinearSolve.jl! However, there is so much more to
+see. Next check out:
+
+- [Some code optimization tricks to know about with NonlinearSolve.jl](@ref code_optimization)
+- [An iterator interface which lets you step through the solving process step by step](@ref iterator)
+- [How to handle large systems of equations efficiently](@ref large_systems)
+- [Ways to use NonlinearSolve.jl that is faster to startup and can statically compile](@ref fast_startup)
+- [More detailed termination conditions](@ref termination_conditions_tutorial)
+
+And also check out the rest of the manual.

docs/src/tutorials/iterator_interface.md

@@ -1,4 +1,8 @@
-# Nonlinear Solver Iterator Interface
+# [Nonlinear Solver Iterator Interface](@id iterator)
+
+!!! warn
+
+    This iterator interface will be expanded with a `step!` function soon!
 
 There is an iterator form of the nonlinear solver which mirrors the DiffEq integrator interface:
 

docs/src/tutorials/advanced.md → docs/src/tutorials/large_systems.md

@@ -1,4 +1,4 @@
-# Solving Large Ill-Conditioned Nonlinear Systems with NonlinearSolve.jl
+# [Solving Large Ill-Conditioned Nonlinear Systems with NonlinearSolve.jl](@id large_systems)
 
 This tutorial is for getting into the extra features of using NonlinearSolve.jl. Solving ill-conditioned nonlinear systems requires specializing the linear solver on properties of the Jacobian in order to cut down on the ``\mathcal{O}(n^3)`` linear solve and the ``\mathcal{O}(n^2)`` back-solves. This tutorial is designed to explain the advanced usage of NonlinearSolve.jl by solving the steady state stiff Brusselator partial differential equation (BRUSS) using NonlinearSolve.jl.
 

docs/src/tutorials/small_compile.md

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+# Faster Startup and and Static Compilation

docs/src/tutorials/termination_conditions.md

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+# [More Detailed Termination Conditions](@id termination_conditions_tutorial)