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author | 2021-01-18 13:25:16 +0000 | |
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committer | 2021-01-18 13:25:16 +0000 | |
commit | cdd8fdc32e730d5a65796a791ff13a92815c59b9 (patch) | |
tree | 3ee2ebf6295a44518d55ee3f6d9e26f4ca0a8a79 /Eigen/src/Core/util | |
parent | bde6741641b7c677d901cd48db844fcea1fd32fe (diff) |
Vectorize `pow(x, y)`. This closes https://gitlab.com/libeigen/eigen/-/issues/2085, which also contains a description of the algorithm.
I ran some testing (comparing to `std::pow(double(x), double(y)))` for `x` in the set of all (positive) floats in the interval `[std::sqrt(std::numeric_limits<float>::min()), std::sqrt(std::numeric_limits<float>::max())]`, and `y` in `{2, sqrt(2), -sqrt(2)}` I get the following error statistics:
```
max_rel_error = 8.34405e-07
rms_rel_error = 2.76654e-07
```
If I widen the range to all normal float I see lower accuracy for arguments where the result is subnormal, e.g. for `y = sqrt(2)`:
```
max_rel_error = 0.666667
rms = 6.8727e-05
count = 1335165689
argmax = 2.56049e-32, 2.10195e-45 != 1.4013e-45
```
which seems reasonable, since these results are subnormals with only couple of significant bits left.
Diffstat (limited to 'Eigen/src/Core/util')
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