Implicit Runge-Kutta Gauss-Legendre 16th order (Julia)
Author SciML
19 Stars
Updated Last
8 Months Ago
Started In
March 2020


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IRKGaussLegendre.jl is an efficient Julia implementation of an implicit Runge-Kutta Gauss-Legendre 16th order method. The method is fully integrated into the DifferentialEquations.jl ecosystem for high-performance high-precision integration.

Required Julia 1.5 version or higher


We present a Julia implementation of a 16th order Implicit Runge-Kutta integrator IRKGL16 (a 8-stage IRK scheme based on Gauss-Legendre nodes) for high accuracy numerical integration of non-stiff ODE systems. Our algorithm supports adaptive timesteping, mixed precision and multithreading to solve problems fast and accuracy

The family of implicit Runge-Kutta schemes based on collocation with Gauss-Legendre nodes are known to be symplectic and super-convergent (order 2s for the method with s internal nodes), and thus very convenient for the high precision numerical integration of Hamiltonian systems with constant time-step size. For non-stiff problems, implementations based on fixed-point iterations are recommended

We believe that, for general (non-necessarily Hamiltonian) non-stiff ODE systems, such implicit Runge-Kutta methods (implemented with fixed point iteration) can be very competitive for high precision computations (for accuracy requirements that exceeds double precision arithmetic)


This package can be installed using

julia>using Pkg
julia>using IRKGaussLegendre

Solver options

Available common arguments

  • dt: stepsize
  • save_everystep: default is true
  • adaptive: =true (adaptive timestepping); =false (fixed timestepping)
  • maxiters: maximum number of iterations before stopping

No-common arguments

  • initial_interp: initialization method for stages. - =false simplest initialization - =true (default) interpolating from the stage values of previous step
  • mstep: output saved at every 'mstep' steps. Default 1.
  • myoutputs: default false
  • maxtrials: maximum number of attempts to accept adaptive step size
  • threading - =false (default): sequential execution of the numerical integration - =true: parallel execution (stage-wise parallelization)
  • mixed_precision - =false (default) - =true: combine 'base precision arithmetic' with precision specified in low_prec_type variable
  • low_prec_type: (Float64, Float32,...)
  • nrmbits: number of bits to remove when applying the stop criterion

Return Codes

The solution types have a retcode field which returns a symbol signifying the error state of the solution. The retcodes are as follows:

  • ReturnCode.Success: The integration completed without erroring.
  • ReturnCode.Failure: General uncategorized failures or errors.

Example: Burrau's problem of three bodies

Three point masses attract each other according to the Newtonian law of gravitation. The masses of the particles are m1=3, m2=4, and m3=5; they are initially located at the apexes of a right triangle with sides 3, 4, and 5, so that the corresponding masses and sides are opposite. The particles are free to move in the plane of the triangle and are at rest initially.

Szebehely, V. 1967, "Burrau's Problem of Three Bodies", Proceedings of the National Academy of Sciences of the United States of America, vol. 58, Issue 1, pp. 60-65 postscript file

Step 1: Defining the problem

To solve this numerically, we define a problem type by giving it the equation, the initial condition, and the timespan to solve over:

using IRKGaussLegendre
using Plots, LinearAlgebra, LaTeXStrings
function NbodyODE!(du,u,Gm,t)
     N = length(Gm)
     du[1,:,:] .= 0
     for i in 1:N
        qi = u[2,:,i]
        Gmi = Gm[i]
        du[2,:,i] = u[1,:,i]
        for j in (i+1):N
           qj = u[2,:,j]
           Gmj = Gm[j]
           qij = qi - qj
           auxij = (qij[1]*qij[1]+qij[2]*qij[2]+qij[3]*qij[3])^(-3/2)
           du[1,:,i] -= Gmj*auxij*qij
           du[1,:,j] += Gmi*auxij*qij

Gm = [5, 4, 3]
q0 = reshape(q,3,:)
v0 = reshape(v,3,:)
u0 = Array{Float64}(undef,2,3,N)
u0[1,:,:] = v0
u0[2,:,:] = q0
tspan = (0.0,63.0)

Step 2: Solving the problem

After defining a problem, you solve it using solve

sol1=solve(prob,IRKGL16(),adaptive=true, reltol=1e-12, abstol=1e-12);

Step 3: Analyzing the solution


bodylist = ["Body-1", "Body-2", "Body-3"]
pl = plot(title="Burrau problem (Adaptive)",aspect_ratio=1)

ulist1 = sol1.u[1:end]
tlist1 = sol1.t[1:end]

for j = 1:3
 xlist  = map(u->u[2,1,j], ulist1)
 ylist  = map(u->u[2,2,j], ulist1)
 pl = plot!(xlist,ylist, label = bodylist[j])   

Burrau problem

Step Size

plot(xlabel="t", ylabel="step size",title="Adaptive step size")
steps1 =sol1.t[2:end]-sol1.t[1:end-1]

Burrau problem


function NbodyEnergy(u,Gm)
     N = length(Gm)
     zerouel = zero(eltype(u))
     T = zerouel
     U = zerouel
     for i in 1:N
        qi = u[2,:,i]
        vi = u[1,:,i]
        Gmi = Gm[i]
        T += Gmi*(vi[1]*vi[1]+vi[2]*vi[2]+vi[3]*vi[3])
        for j in (i+1):N
           qj = u[2,:,j]  
           Gmj = Gm[j]
           qij = qi - qj
           U -= Gmi*Gmj/norm(qij)
    1/2*T + U
setprecision(BigFloat, 256)

ΔE1 = map(x->NbodyEnergy(BigFloat.(x),GmBig), sol1.u)./E0.-1
plot(title="Energy error", xlabel="t", ylabel=L"\Delta E")
plot!(sol1.t,log10.(abs.(ΔE1)), label="")

Burrau problem

More Examples

Benchmark examples

Implementation details

Antoñana, M., Makazaga, J., Murua, Ander. "Reducing and monitoring round-off error propagation for symplectic implicit Runge-Kutta schemes." Numerical Algorithms. 2017.