# OREGO Work-Precision Diagrams

##### Chris Rackauckas
using OrdinaryDiffEq, DiffEqDevTools, ParameterizedFunctions, Plots, ODE, ODEInterfaceDiffEq, LSODA, Sundials
gr() #gr(fmt=:png)
using LinearAlgebra

f = @ode_def Orego begin
dy1 = p1*(y2+y1*(1-p2*y1-y2))
dy2 = (y3-(1+y1)*y2)/p1
dy3 = p3*(y1-y3)
end p1 p2 p3

p = [77.27,8.375e-6,0.161]
prob = ODEProblem(f,[1.0,2.0,3.0],(0.0,30.0),p)
sol = solve(prob,Rodas5(),abstol=1/10^14,reltol=1/10^14)
test_sol = TestSolution(sol)
abstols = 1.0 ./ 10.0 .^ (4:11)
reltols = 1.0 ./ 10.0 .^ (1:8);

plot_prob = ODEProblem(f,[1.0,2.0,3.0],(0.0,400.0),p)
sol = solve(plot_prob,CVODE_BDF())
plot(sol,yscale=:log10)


## Omissions and Tweaking

The following were omitted from the tests due to convergence failures. ODE.jl's adaptivity is not able to stabilize its algorithms, while GeometricIntegratorsDiffEq has not upgraded to Julia 1.0. GeometricIntegrators.jl's methods used to be either fail to converge at comparable dts (or on some computers errors due to type conversions).

#sol = solve(prob,ode23s()); println("Total ODE.jl steps: \$(length(sol))")
#using GeometricIntegratorsDiffEq
#try
#catch e
#    println(e)
#end

sol = solve(prob,ARKODE(),abstol=1e-5,reltol=1e-1);

sol = solve(prob,ARKODE(nonlinear_convergence_coefficient = 1e-3),abstol=1e-5,reltol=1e-1);

sol = solve(prob,ARKODE(order=3),abstol=1e-5,reltol=1e-1);

sol = solve(prob,ARKODE(order=3,nonlinear_convergence_coefficient = 1e-5),abstol=1e-5,reltol=1e-1);

sol = solve(prob,ARKODE(order=5),abstol=1e-5,reltol=1e-1);


## High Tolerances

This is the speed when you just want the answer.

solve(prob, ddebdf())
solve(prob, rodas())
abstols = 1.0 ./ 10.0 .^ (5:8)
reltols = 1.0 ./ 10.0 .^ (1:4);
setups = [Dict(:alg=>Rosenbrock23()),
Dict(:alg=>Rodas3()),
Dict(:alg=>TRBDF2()),
Dict(:alg=>CVODE_BDF()),
Dict(:alg=>rodas()),
Dict(:alg=>lsoda()),
Dict(:alg=>ROCK2()),
Dict(:alg=>ROCK4())
]
wp = WorkPrecisionSet(prob,abstols,reltols,setups;
save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;dense = false,verbose=false,
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:l2,numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:L2,numruns=10)
plot(wp)

setups = [Dict(:alg=>Rosenbrock23()),
Dict(:alg=>Kvaerno3()),
Dict(:alg=>CVODE_BDF()),
Dict(:alg=>KenCarp4()),
Dict(:alg=>TRBDF2()),
Dict(:alg=>KenCarp3()),
# Dict(:alg=>SDIRK2()), # Removed because it's bad
wp = WorkPrecisionSet(prob,abstols,reltols,setups;
save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;dense = false,verbose = false,
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:l2,numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:L2,numruns=10)
plot(wp)

setups = [Dict(:alg=>Rosenbrock23()),
Dict(:alg=>KenCarp5()),
Dict(:alg=>KenCarp4()),
Dict(:alg=>KenCarp3()),
Dict(:alg=>ARKODE(order=5)),
Dict(:alg=>ARKODE(nonlinear_convergence_coefficient = 1e-6)),
Dict(:alg=>ARKODE(nonlinear_convergence_coefficient = 1e-5,order=3))
]
names = ["Rosenbrock23" "KenCarp5" "KenCarp4" "KenCarp3" "ARKODE5" "ARKODE4" "ARKODE3"]
wp = WorkPrecisionSet(prob,abstols,reltols,setups;
names=names,
save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
plot(wp)


### Low Tolerances

This is the speed at lower tolerances, measuring what's good when accuracy is needed.

abstols = 1.0 ./ 10.0 .^ (7:13)
reltols = 1.0 ./ 10.0 .^ (4:10)

setups = [Dict(:alg=>GRK4A()),
Dict(:alg=>Rodas4P()),
Dict(:alg=>CVODE_BDF()),
Dict(:alg=>ddebdf()),
Dict(:alg=>Rodas4()),
Dict(:alg=>rodas()),
Dict(:alg=>lsoda()),
#Dict(:alg=>ROCK2()), #Needs more iterations
Dict(:alg=>ROCK4())
]
wp = WorkPrecisionSet(prob,abstols,reltols,setups;
save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;verbose=false,
dense=false,appxsol=test_sol,maxiters=Int(1e5),error_estimate=:l2,numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:L2,numruns=10)
plot(wp)

setups = [
Dict(:alg=>Rodas5()),
Dict(:alg=>Kvaerno5()),
Dict(:alg=>CVODE_BDF()),
Dict(:alg=>KenCarp4()),
Dict(:alg=>KenCarp5()),
Dict(:alg=>Rodas4()),
wp = WorkPrecisionSet(prob,abstols,reltols,setups;
save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;verbose=false,
dense=false,appxsol=test_sol,maxiters=Int(1e5),error_estimate=:l2,numruns=10)
plot(wp)

wp = WorkPrecisionSet(prob,abstols,reltols,setups;
appxsol=test_sol,maxiters=Int(1e5),error_estimate=:L2,numruns=10)
plot(wp)


The following algorithms were removed since they failed.

#setups = [Dict(:alg=>Hairer4()),
#Dict(:alg=>Hairer42()),
#Dict(:alg=>Rodas3()),
#Dict(:alg=>Kvaerno4()),
#Dict(:alg=>Cash4())
#]
#wp = WorkPrecisionSet(prob,abstols,reltols,setups;
#                      save_everystep=false,appxsol=test_sol,maxiters=Int(1e5),numruns=10)
#plot(wp)


### Conclusion

At high tolerances, Rosenbrock23 hits the the error estimates and is fast. At lower tolerances and normal user tolerances, Rodas4 and Rodas5 are extremely fast. When you get down to reltol=1e-9 radau begins to become as efficient as Rodas4, and it continues to do well below that.

using DiffEqBenchmarks
DiffEqBenchmarks.bench_footer(WEAVE_ARGS[:folder],WEAVE_ARGS[:file])


## Appendix

These benchmarks are a part of the DiffEqBenchmarks.jl repository, found at: https://github.com/JuliaDiffEq/DiffEqBenchmarks.jl

To locally run this tutorial, do the following commands:

using DiffEqBenchmarks
DiffEqBenchmarks.weave_file("StiffODE","Orego.jmd")

Computer Information:

Julia Version 1.1.0
Commit 80516ca202 (2019-01-21 21:24 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, haswell)


Package Information:

Status: /home/crackauckas/.julia/environments/v1.1/Project.toml
[c52e3926-4ff0-5f6e-af25-54175e0327b1] Atom 0.8.7
[bcd4f6db-9728-5f36-b5f7-82caef46ccdb] DelayDiffEq 5.4.1
[bb2cbb15-79fc-5d1e-9bf1-8ae49c7c1650] DiffEqBenchmarks 0.1.0
[459566f4-90b8-5000-8ac3-15dfb0a30def] DiffEqCallbacks 2.5.2
[f3b72e0c-5b89-59e1-b016-84e28bfd966d] DiffEqDevTools 2.8.0
[aae7a2af-3d4f-5e19-a356-7da93b79d9d0] DiffEqFlux 0.5.0
[78ddff82-25fc-5f2b-89aa-309469cbf16f] DiffEqMonteCarlo 0.15.1
[77a26b50-5914-5dd7-bc55-306e6241c503] DiffEqNoiseProcess 3.3.1
[055956cb-9e8b-5191-98cc-73ae4a59e68a] DiffEqPhysics 3.1.0
[a077e3f3-b75c-5d7f-a0c6-6bc4c8ec64a9] DiffEqProblemLibrary 4.1.0
[0c46a032-eb83-5123-abaf-570d42b7fbaa] DifferentialEquations 6.4.0
[b305315f-e792-5b7a-8f41-49f472929428] Elliptic 0.5.0
[7f56f5a3-f504-529b-bc02-0b1fe5e64312] LSODA 0.4.0
[c030b06c-0b6d-57c2-b091-7029874bd033] ODE 2.4.0
[54ca160b-1b9f-5127-a996-1867f4bc2a2c] ODEInterface 0.4.5
[09606e27-ecf5-54fc-bb29-004bd9f985bf] ODEInterfaceDiffEq 3.3.1
[1dea7af3-3e70-54e6-95c3-0bf5283fa5ed] OrdinaryDiffEq 5.8.1
[2dcacdae-9679-587a-88bb-8b444fb7085b] ParallelDataTransfer 0.5.0
[65888b18-ceab-5e60-b2b9-181511a3b968] ParameterizedFunctions 4.1.1
[91a5bcdd-55d7-5caf-9e0b-520d859cae80] Plots 0.25.2
[d330b81b-6aea-500a-939a-2ce795aea3ee] PyPlot 2.8.1
[731186ca-8d62-57ce-b412-fbd966d074cd] RecursiveArrayTools 0.20.0
[90137ffa-7385-5640-81b9-e52037218182] StaticArrays 0.11.0
[e88e6eb3-aa80-5325-afca-941959d7151f] Zygote 0.3.2