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https://issues.apache.org/jira/browse/MATH-375?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12903722#action_12903722
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Luc Maisonobe commented on MATH-375:
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We finally get the grant, thanks.
I have started integrating the patch in our current code base (not committed
yet).
I added all missing functions with default implementations (either simple
delegates to java.util.Math or basic implementations).
The code has also been edited to comply to our standard code style
(indentation, braces, trailing spaces, variables naming, this kind of stuff).
Concerning the unit tests, I cannot commit them due to the external libraries
and their license. I will start a discussion on the dev list about it.
Using the existing tests with all their dependencies on my machine, I had to
slightly increase the threshold from 0.502 ULP to 0.504 ULP as the initial
value lead to random failures. The tests were done on a 64 bits linux computer
with Java 6. Are suche random failure expected ?
> Elementary functions in JDK are slower than necessary and not as accurate as
> they could be.
> -------------------------------------------------------------------------------------------
>
> Key: MATH-375
> URL: https://issues.apache.org/jira/browse/MATH-375
> Project: Commons Math
> Issue Type: New Feature
> Environment: JDK 1.4 - 1.6
> Reporter: William Rossi
> Fix For: 2.2
>
> Attachments: atanpatch.txt.gz, FastMath.tar.gz
>
>
> I would like to contribute improved versions on exp(), log(), pow(), etc. to
> the project. Please refer to this discussion thread
> http://markmail.org/message/zyeoguw6gwtofm62.
> I have developed over the past year a set of elementary functions similar to
> those in java.lang.Math, but with the following characteristics:
> * Higher performance.
> * Better accuracy. Results are accurate to slightly more that +/- 0.5 ULP.
> * Pure Java. The standard Math class is impleneted via JNI, and thus takes a
> performance hit.
> Note that some functions such as exp are nearly twice as fast in my
> implementation. I've seen it 3 times faster on different processors. The
> preformance varies by the relative speed of calculation vs memory lookups.
> The functions are implemented as tables of values in extra precision (approx
> 70 bits), and then interpolated with a minimax polynomial.
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