| commit | cdd8fdc32e730d5a65796a791ff13a92815c59b9 | [log] [tgz] |
|---|---|---|
| author | Rasmus Munk Larsen <rmlarsen@google.com> | Mon Jan 18 13:25:16 2021 +0000 |
| committer | David Tellenbach <david.tellenbach@me.com> | Mon Jan 18 13:25:16 2021 +0000 |
| tree | 3ee2ebf6295a44518d55ee3f6d9e26f4ca0a8a79 | |
| 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.
Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
For more information go to http://eigen.tuxfamily.org/.
For pull request, bug reports, and feature requests, go to https://gitlab.com/libeigen/eigen.