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.

1 files changed, 1 insertions, 1 deletions

diff --git a/Eigen/src/Core/arch/AVX512/PacketMath.h b/Eigen/src/Core/arch/AVX512/PacketMath.h
index f8388579b..fa43d8809 100644
--- a/Eigen/src/Core/arch/AVX512/PacketMath.h
+++ b/Eigen/src/Core/arch/AVX512/PacketMath.h
@@ -88,7 +88,7 @@ struct packet_traits<half> : default_packet_traits {
     HasCeil   = 1,
     HasRint   = 1,
     HasBessel = 1,
-    HasNdtri  = 1,
+    HasNdtri  = 1
   };
 };