1D example about integrals.jl HOT 9 CLOSED

You need to do solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3).u. But it's having an issue that the domain transformation is increasing nout? @agerlach is that supposed to happen?

from integrals.jl.

I'll take a look at it. May not be until tomorrow though.

from integrals.jl.

But it's having an issue that the domain transformation is increasing nout? @agerlach is that supposed to happen?

Yes. B/c the gradient is computed as the integral of the vector of partials. Here length(p)=2, so nout gets changed to 2. QuadGKJL() does not support vector valued integrands. So, @frank-ccc if you swap out QuadGKJL() for any of the other methods it will work as expected, e.q.

function testf(lb,ub,p)
    prob = QuadratureProblem(f,lb,ub,p)
    solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3)[1]
end

function testf2(lb,ub,p)
    prob = QuadratureProblem(f,lb,ub,p)
    solve(prob,CubatureJLh(),reltol=1e-3,abstol=1e-3)[1]
end

dp1 = Zygote.gradient(p->testf(lb,ub,p),p)[1]
dp2 = Zygote.gradient(p->testf2(lb,ub,p),p)[1]

from integrals.jl.

Oh hmm, it would be good to find an informative error message here, or break it apart on QuadGK

from integrals.jl.

@ChrisRackauckas @agerlach thanks a lot to help resolve this. I am actually trying to solve a Fredholm problem with differential programming. May I contact you separately if have questions?

from integrals.jl.

Yeah sure

from integrals.jl.

@ChrisRackauckas It appears this issue was fixed by #189 since we no longer use nout as a check on QuadGKJL. The following translation of the mwe is working for me

using Integrals, Cuba, Cubature, Zygote, FiniteDiff, ForwardDiff
using Test

f(y,p) = sum(sin.(p[1].*y) + p[2])
lb = 1.0
ub = 3.0
p = [2.0,1.0]
prob = IntegralProblem(f,lb,ub,p)
sol = solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3)

function testf(lb,ub,p)
    prob = IntegralProblem(f,lb,ub,p)
    solve(prob,QuadGKJL(),reltol=1e-3,abstol=1e-3).u
end

function testf2(lb,ub,p)
    prob = IntegralProblem(f,lb,ub,p)
    solve(prob,CubatureJLh(),reltol=1e-3,abstol=1e-3).u
end

dlb1,dub1,dp1 = Zygote.gradient(testf,lb,ub,p)
dlb2,dub2,dp2 = Zygote.gradient(testf2,lb,ub,p)

@test dlb1 ≈ dlb2
@test dub1 ≈ dub2
@test dp1 ≈ dp2

I will follow up on this with a pr to run the existing AD tests on all the compatible algorithms, which would be helpful at catching edge cases.

from integrals.jl.

Awesome, really nice job on that update PR. Seems like it fixed most issues 😅

from integrals.jl.

Can confirm that this is fixed by #189 and will close now that we have #196 for better testing

from integrals.jl.