# Feagin's Order 10, 12, and 14 Methods

##### Chris Rackauckas

DifferentialEquations.jl includes Feagin's explicit Runge-Kutta methods of orders 10/8, 12/10, and 14/12. These methods have such high order that it's pretty much required that one uses numbers with more precision than Float64. As a prerequisite reference on how to use arbitrary number systems (including higher precision) in the numerical solvers, please see the Solving Equations in With Chosen Number Types notebook.

## Investigation of the Method's Error

We can use Feagin's order 16 method as follows. Let's use a two-dimensional linear ODE. Like in the Solving Equations in With Chosen Number Types notebook, we change the initial condition to BigFloats to tell the solver to use BigFloat types.

using DifferentialEquations
const linear_bigα = big(1.01)
f(u,p,t) = (linear_bigα*u)

# Add analytical solution so that errors are checked
f_analytic(u0,p,t) = u0*exp(linear_bigα*t)
ff = ODEFunction(f,analytic=f_analytic)
prob = ODEProblem(ff,big(0.5),(0.0,1.0))

println(sol.errors)

Dict{Symbol, BigFloat}(:l∞ => 2.1975104034266099178147026326495605606836593
67683780324635801610297349872909655e-23, :final => 2.1975104034266099178147
02632649560560683659367683780324635801610297349872909655e-23, :l2 => 1.0615
01597814768635894514677590712762248364686527596359902826841740549975688161e
-23)


Compare that to machine $\epsilon$ for Float64:

eps(Float64)

2.220446049250313e-16


The error for Feagin's method when the stepsize is 1/16 is 8 orders of magnitude below machine $\epsilon$! However, that is dependent on the stepsize. If we instead use adaptive timestepping with the default tolerances, we get

sol =solve(prob,Feagin14());
println(sol.errors); print("The length was \$(length(sol))")

Dict{Symbol, BigFloat}(:l∞ => 1.5457388839431409625465375986097592198164147
90728029220638828884206395861982752e-09, :final => 1.5457388839431409625465
37598609759219816414790728029220638828884206395861982752e-09, :l2 => 8.9250
66870202330409924421192162193462506388332261074725109949218067763405137993e
-10)
The length was 3


Notice that when the stepsize is much higher, the error goes up quickly as well. These super high order methods are best when used to gain really accurate approximations (using still modest timesteps). Some examples of where such precision is necessary is astrodynamics where the many-body problem is highly chaotic and thus sensitive to small errors.

## Convergence Test

The Order 14 method is awesome, but we need to make sure it's really that awesome. The following convergence test is used in the package tests in order to make sure the implementation is correct. Note that all methods have such tests in place.

using DiffEqDevTools
dts = 1.0 ./ 2.0 .^(10:-1:4)
sim = test_convergence(dts,prob,Feagin14())

DiffEqDevTools.ConvergenceSimulation{SciMLBase.ODESolution{BigFloat, 1, Vec
tor{BigFloat}, Vector{BigFloat}, Dict{Symbol, BigFloat}, Vector{Float64}, V
ector{Vector{BigFloat}}, SciMLBase.ODEProblem{BigFloat, Tuple{Float64, Floa
t64}, false, SciMLBase.NullParameters, SciMLBase.ODEFunction{false, typeof(
Main.##WeaveSandBox#1124.f), LinearAlgebra.UniformScaling{Bool}, typeof(Mai
n.##WeaveSandBox#1124.f_analytic), Nothing, Nothing, Nothing, Nothing, Noth
ing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase
.DEFAULT_OBSERVED), Nothing}, Base.Iterators.Pairs{Union{}, Union{}, Tuple{
}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.
Feagin14, OrdinaryDiffEq.InterpolationData{SciMLBase.ODEFunction{false, typ
eof(Main.##WeaveSandBox#1124.f), LinearAlgebra.UniformScaling{Bool}, typeof
(Main.##WeaveSandBox#1124.f_analytic), Nothing, Nothing, Nothing, Nothing,
Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciML
Base.DEFAULT_OBSERVED), Nothing}, Vector{BigFloat}, Vector{Float64}, Vector
{Vector{BigFloat}}, OrdinaryDiffEq.Feagin14ConstantCache{BigFloat, Float64}
}, DiffEqBase.DEStats}}(SciMLBase.ODESolution{BigFloat, 1, Vector{BigFloat}
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BigFloat}}, SciMLBase.ODEProblem{BigFloat, Tuple{Float64, Float64}, false,
SciMLBase.NullParameters, SciMLBase.ODEFunction{false, typeof(Main.##WeaveS
andBox#1124.f), LinearAlgebra.UniformScaling{Bool}, typeof(Main.##WeaveSand
Box#1124.f_analytic), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing,
Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSE
RVED), Nothing}, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple
{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.Feagin14, Ord
inaryDiffEq.InterpolationData{SciMLBase.ODEFunction{false, typeof(Main.##We
aveSandBox#1124.f), LinearAlgebra.UniformScaling{Bool}, typeof(Main.##Weave
SandBox#1124.f_analytic), Nothing, Nothing, Nothing, Nothing, Nothing, Noth
ing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_
OBSERVED), Nothing}, Vector{BigFloat}, Vector{Float64}, Vector{Vector{BigFl
oat}}, OrdinaryDiffEq.Feagin14ConstantCache{BigFloat, Float64}}, DiffEqBase
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5745507598647e-33, 4.965060749672954837420128986603252643572639175648660423
260171211890675951960709e-28, 2.1975104034266099178147026326495605606836593
67683780324635801610297349872909655e-23], :l2 => BigFloat[1.557658061895966
325846207347700821566122250234951867982385249845278493662676944e-49, 2.3604
11657197547333498547223212880765989953910523198376084992961121455787643313e
-45, 3.24060760516074676637178554271828070823716806609832930426777898390162
3487961701e-41, 3.264565979149024498598621687244084221464920048688554495210
368172485686228822379e-37, 1.2947776667473864852636114197311110560713898649
84176915402703871046417929063523e-33, 2.35148503019100306142594944698233564
8801181524332244933545614443786091245762492e-28, 1.061501597814768635894514
677590712762248364686527596359902826841740549975688161e-23]), 7, Dict(:dts
=> [0.0009765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.
0625]), Dict{Any, Any}(:l∞ => 14.293327546103852435000893132848160405565048
16254374715376150534187461411604701, :final => 14.2933275461038524350008931
3284816040556504816254374715376150534187461411604701, :l2 => 14.30280974051
840423232019057634315242594313233119811212889763182960978082577142), [0.000
9765625, 0.001953125, 0.00390625, 0.0078125, 0.015625, 0.03125, 0.0625])


For a view of what's going on, let's plot the simulation results.

using Plots
gr()
plot(sim)


This is a clear trend indicating that the convergence is truly Order 14, which is the estimated slope.

## Appendix

These tutorials are a part of the SciMLTutorials.jl repository, found at: https://github.com/SciML/SciMLTutorials.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.

To locally run this tutorial, do the following commands:

using SciMLTutorials
SciMLTutorials.weave_file("tutorials/ode_extras","02-feagin.jmd")

Computer Information:

Julia Version 1.6.2
Commit 1b93d53fc4 (2021-07-14 15:36 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: AMD EPYC 7502 32-Core Processor
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, znver2)
Environment:
JULIA_DEPOT_PATH = /root/.cache/julia-buildkite-plugin/depots/a6029d3a-f78b-41ea-bc97-28aa57c6c6ea


Package Information:

      Status /var/lib/buildkite-agent/builds/5-amdci4-julia-csail-mit-edu/julialang/scimltutorials-dot-jl/tutorials/ode_extras/Project.toml
[f3b72e0c] DiffEqDevTools v2.27.2
[0c46a032] DifferentialEquations v6.17.1
[961ee093] ModelingToolkit v5.17.3
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[429524aa] Optim v1.3.0
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[91a5bcdd] Plots v1.15.2
[30cb0354] SciMLTutorials v0.9.0
[37e2e46d] LinearAlgebra
[2f01184e] SparseArrays

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