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Fix for Cofunction self-assignment via interpolation #3939

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Fixes #3935

a = assemble(TestFunction(V1) * dx)
b = assemble(TestFunction(V1) * dx)
a.interpolate(a)
assert (a.dat.data_ro == b.dat.data_ro).all()
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Not sure if this should be a np.allclose test

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I think you're right as written. No flops should be performed so that should be an equality.

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I don't think it's actually no flops, it's interpolation which happens to be the identify except for roundoff.

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Changed to np.allclose, I'm pretty sure now that's correct

firedrake/interpolation.py Outdated Show resolved Hide resolved
a = assemble(TestFunction(V1) * dx)
b = assemble(TestFunction(V1) * dx)
a.interpolate(a)
assert (a.dat.data_ro == b.dat.data_ro).all()
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I think you're right as written. No flops should be performed so that should be an equality.

if x.id != out.id:
mul(x, out)
else:
out_ = out.duplicate()
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Shouldn't a no-op be the right thing?

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I don't think so, e.g.

u = fd.Function(space, name="u")
v = fd.Function(space, name="v")
w = fd.Cofunction(space.dual(), name="w")
...
fd.assemble(fd.action(
    fd.adjoint(fd.derivative(interpolate(v * u, space), u)),
    w), tensor=w)

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I see, it only is a no-op in the MFE from the issue: assemble(L(w), tensor=w) with L being the identity.

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Maybe it'd be good to add a more complicated test then

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Done. I think there are also be issues with complex here (transpose should be Hermitian for the adjoint actions?).

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BUG: Self-assignment via Cofunction.interpolate
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