Error using SplitODEProblem: type GMRESIterable has no field reltol

Hi,

I have been trying to use a SplitODEProblem to solve the equations of motion of a pulse propagating along a transmission line but I get the following message:
ERROR: LoadError: type GMRESIterable has no field reltol
I am using DifferentialEquation v6.16.0.
Here’s a code snippet that reproduces the issue:

using DifferentialEquations

struct TL2
    L::Float64
    C::Float64
    Rl::Float64
    N::Int
end

struct Drive2{F}
    Rd::Float64    
    waveform::F
end

struct GaussianPulse2
    amp::Float64
    t0::Float64
    width::Float64   
end   

V(s::GaussianPulse2, t)  = s.amp*exp.( -(t-s.t0)^2/(2*s.width) )/sqrt(2π*s.width) 
(s::GaussianPulse2)(t) = V(s, t)

struct Circuit2{F}
    tl::TL2
    drive::Drive2{F}
end

function matrix_tl(tl::TL2)
    L = tl.L 
    C = tl.C 
    N = tl.N    
    Rl = tl.Rl 

    A = zeros(2*(N+1)+2, 2*(N+1)+2)
    A[1,3] = 1.0
    A[N+3, N+2] = 1.0/Rl/C 
    A[N+3, N+3] = -1.0/Rl/C    
    for i in 3:N+2
        A[i,(N+1)+ i] = 1.0/L
        A[i,(N+1)+ i+1] = -1.0/L
        A[(N+1)+ i + 1, i ] = 1.0/C
        A[(N+1)+ i + 1, i+1] = -1.0/C
    end
    return DiffEqArrayOperator(A)
end

matrix_tl(circuit::Circuit2) = matrix_tl(circuit.tl)

function drive_tl(dy, y, circuit::Circuit2, t)    
    Vd_dot = gradient(circuit.drive.waveform, t)[1]
    Rd = circuit.drive.Rd     
    N = circuit.tl.N  
    dy[2 + (N+1) + 1] = Vd_dot - Rd*dy[3] - dy[2]    
end

g = GaussianPulse2(0.3, 1.0, 1e-3)
drive = Drive2(50.0, g)
tl = TL2(0.02, 0.005, 0.008, 51)
circuit = Circuit2(tl, drive)

y0 = zeros(2*(tl.N+1)+2)
prob = SplitODEProblem(matrix_tl(circuit), drive_tl, y0, (0.0, 5.0),  circuit)
sol = solve(prob, tstops=(1.0))

Thanks for letting me know if you how how to fix this error.

P.S. Any comments that would help speedup the code is also highly appreciated

Use Julia v1.5 and the latest package versions. This is just a versioning error. Delete packages holding things back. Share ]st and ]st -m if you need help identifying the offending package.

Your problem is unstable though, so when it solves it diverges. dy[2 + (N+1) + 1] = Vd_dot - Rd*dy[3] - dy[2] as a line doesn’t make sense in an ODE because dy[3] and dy[2] are undefined here, so this has a random divergent solution.

Thanks @ChrisRackauckas. Guidance on how to fix the versioning error is appreciated, here is the ]st:

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and of ]st -m;

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  [c5fb5394] Xorg_xtrans_jll v1.4.0+3
  [c2297ded] ZMQ v1.2.1
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Concerning the fact that dy[3] and dy[2] are undefined, it’s partly due to a mistake when I simplified the problem before posting it and partly because I thought that the function drive_tl had access to the dy defined by DiffEqArrayOperator(A)*y

Thanks

It looks like the error type GMRESIterable has no field reltol was caused by a using OrdinaryDiffEq that was ran in a file included above the code snippet that I shared (i.e. seems like there was a conflict between OrdinaryDiffEq and DifferentialEquations). Replacing OrdinaryDiffEq by DifferentialEquations fixed the issue.

I run into an unstability problem however if I try to run the code defined as a SplitODEProblem whereas I get the expected result using the problem defined in term of an ODEProblem. here’s the code:

using DifferentialEquations
using Zygote
using Plots

struct TL2
    L::Float64
    C::Float64
    Rl::Float64
    N::Int
end

struct Drive2{F}
    Rd::Float64    
    waveform::F
end

struct GaussianPulse2
    amp::Float64
    t0::Float64
    width::Float64   
end   

V(s::GaussianPulse2, t)  = s.amp*exp.( -(t-s.t0)^2/(2*s.width) )/sqrt(2π*s.width) 
(s::GaussianPulse2)(t) = V(s, t)

struct Circuit2{F}
    tl::TL2
    drive::Drive2{F}
end

function matrix_tl(tl::TL2)
    L = tl.L 
    C = tl.C 
    N = tl.N    
    Rl = tl.Rl 

    A = zeros(2*(N+1)+2, 2*(N+1)+2)
    A[1,3] = 1.0
    A[N+3, N+2] = 1.0/Rl/C 
    A[N+3, N+3] = -1.0/Rl/C
    A[(N+1)+ 3 + 1, 3 ] = 1.0/C
    A[(N+1)+ 3 + 1, 3+1] = -1.0/C    
    for i in 4:N+2
        A[i,(N+1)+ i] = 1.0/L
        A[i,(N+1)+ i+1] = -1.0/L
        A[(N+1)+ i + 1, i ] = 1.0/C
        A[(N+1)+ i + 1, i+1] = -1.0/C
    end

    return DiffEqArrayOperator(A)
end

matrix_tl(circuit::Circuit2) = matrix_tl(circuit.tl)

function drive_tl(dy, y, circuit::Circuit2, t)    
    Vd_dot = gradient(circuit.drive.waveform, t)[1]
    Rd = circuit.drive.Rd     
    N = circuit.tl.N
    L = circuit.tl.L  

    dy[3] = (y[N+4]-y[N+5])/L
    dy[2] = 0.0  # a non-linear function in the general case
    dy[2 + (N+1) + 1] = Vd_dot - Rd*dy[3] - dy[2]     
    
end

##

function eom!(dy,y, circuit::Circuit2,t)
    L = circuit.tl.L 
    C = circuit.tl.C 
    N = circuit.tl.N   
    Rl = circuit.tl.Rl    

    dy[1] = y[3]
    dy[2] =  0.0 # a non-linear function in the general case
    dy[3] = (y[(N+3) + 1] - y[(N+3) + 2])/L 
    dy[(N+3) + 1] = gradient(circuit.drive.waveform, t)[1] - circuit.drive.Rd*dy[3] - dy[2]

    @inbounds @simd for k = 2:N 
        dy[k + 2] = (y[(N+3) + k] - y[(N+3) + k+1])/L
        dy[k + (N+3)] = (y[2 + k-1] - y[2 + k])/C
    end

    dy[(N+3)] = (y[N+2] - y[N+3])/Rl/C
    
    dy[(N+1) + (N+3)] = (y[2 + (N+1)-1] - y[2 + (N+1)])/C
end;

##
t0 = 0.50
tend = 3.0
g = GaussianPulse2(0.3, t0, 1e-3)
drive = Drive2(50.0, g)
tl = TL2(0.02, 0.005, 0.008, 51)
circuit = Circuit2(tl, drive)
y0 = zeros(2*(tl.N+1)+2)
##
prob = ODEProblem(eom!, y0, (0.0, tend),  circuit)
sol = solve(prob, tstops=(t0)); 
##
split_prob = SplitODEProblem(matrix_tl(circuit), drive_tl, y0, (0.0, tend),  circuit)
split_sol = solve(split_prob, tstops=(t0))

So I suppose my question is now: how do I get rid of the unstability issue when using a SplitODEProblem and should I expect any performance improvement using a SplitODEProblem (provided it works) vs ODEProblem?

You are 2 major versions on ModelingToolkit behind. If you do ]add ModelingToolkit@5 it’ll probably tell you what the offending package is.

Which ODE solver is being used in the two cases? sol.alg will say. My guess is we might need a better default on SplitODEProblem.

Looks like ModelingToolkit installed without any issue:

add ModelingToolkit@5

    Updating registry at `C:\Users\bjd3\.julia\registries\General`
    Updating git-repo `https://github.com/JuliaRegistries/General.git`
   Resolving package versions...
    Updating `K:\bjd3\Test\Project.toml`
  [961ee093] + ModelingToolkit v5.6.4
  No Changes to `K:\bjd3\Test\Manifest.toml`

Here’s the algorithm that works well for the ODEProblem:

CompositeAlgorithm{Tuple{Tsit5, Rosenbrock23{0, false, DefaultLinSolve, DataType}}, OrdinaryDiffEq.AutoSwitchCache{Tsit5, Rosenbrock23{0, false, DefaultLinSolve, DataType}, Rational{Int64}, Int64}}((Tsit5(), Rosenbrock23{0, false, DefaultLinSolve, DataType}(DefaultLinSolve(nothing, nothing), Val{:forward})), OrdinaryDiffEq.AutoSwitchCache{Tsit5, Rosenbrock23{0, false, DefaultLinSolve, DataType}, Rational{Int64}, Int64}(9553, Tsit5(), Rosenbrock23{0, false, DefaultLinSolve, DataType}(DefaultLinSolve(nothing, nothing), Val{:forward}), true, 10, 3, 9//10, 9//10, 2, false))

and for the SplitODEProblem:

KenCarp4{0, false, DefaultLinSolve, NLNewton{Rational{Int64}, Rational{Int64}, Rational{Int64}}, DataType}(DefaultLinSolve(nothing, nothing), NLNewton{Rational{Int64}, Rational{Int64}, Rational{Int64}}(1//100, 10, 1//5, 1//5), Val{:forward}, true, :linear, :PI)

Also, following Handling Instability When Solving ODE Problems - #5 by ChrisRackauckas,
I tried

split_sol = solve(split_prob, tstops=(t0), Rodas4(autodiff=false)) 

with split_prob defined as previously posted. It breaks after 6 points and I get:

Warning: dt <= dtmin. Aborting. There is either an error in your model specification or the true solution is unstable.
└ @ SciMLBase C:\Users\bjd3\.julia\packages\SciMLBase\sRVBG\src\integrator_interface.jl:342

I also tried
split_sol = solve(split_prob, tstops=(t0), Rosenbrock23(autodiff=false))
but that was still running after a few minutes whereas the ODEProblem approach solves in less than a minute so I killed it before it was over… I can time it if you think it’d be useful to see if the integration finally gets done.

Are you sure the second part of the problem is non-stiff?