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Computational time for Brownian Interval #109
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So this is a bit weird! In particular because one tends to imagine a The reason has to do with the binary tree heuristic built in to the The Why different number of steps between these apparently-scale-invariant cases? Floating point inaccuracies: >>> import torch
>>> x = torch.tensor(0.01)
>>> sum = 0
>>> for _ in range(100):
... sum = sum + x
...
>>> sum
tensor(1.0000)
>>> sum < 1
tensor(True) The real bug here is simply that the heuristic takes so much time to compute. I'll need to have a deeper look, later, to figure out what might be done to resolve this. |
I observed something strange about computation time for brownain interval
where the sde is very similar to the one defined in the Quick example in README. In the above three examples, I change the different
ts
anddt
. I think they should have roughly the same computation time. But it turns out the time used by the line are very different. According to the paper, the worse case should roughly be O(log T/dt) if I understand correctly. Why the first case is so slow?The text was updated successfully, but these errors were encountered: