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https://issues.apache.org/jira/browse/MATH-1325?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15302659#comment-15302659
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Eric Barnhill commented on MATH-1325:
-------------------------------------
Hi Fran,
I pulled your latest changes, but running "mvn package", got these errors:
Results :
Failed tests:
LocalizedFormatsTest.testAllKeysPresentInPropertiesFiles:47 missing key
"NUMBER_ODD" for language fr
LocalizedFormatsTest.testMessageNumber:32 expected:<328> but was:<329>
Tests in error:
FiniteDifferenceTest.testLeftMultiplier:64 » MathIllegalArgument 3 is odd
FiniteDifferenceTest.testLength:31 » MathIllegalArgument 3 is odd
FiniteDifferenceTest.testRightMultiplier:97 » MathIllegalArgument 3 is odd
Tests run: 6203, Failures: 2, Errors: 3, Skipped: 43
Just want to make sure it's okay to experiment with the code anyway?
> Improve finite differencing infrastructure
> ------------------------------------------
>
> Key: MATH-1325
> URL: https://issues.apache.org/jira/browse/MATH-1325
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Fran Lattanzio
> Priority: Minor
>
> The existing finite difference framework in commons math is a limiting
> because it accepts only fixed bandwidth parameters. Furthermore, the finite
> difference coefficients/descriptions are not exposed to the user in any
> reasonable fashion (e.g. a user doing a numerical ODE solve probably wants to
> just grab suitable coefficients from somewhere).
> Conceptually, I think the work of finite difference can be broadly divided
> into three tasks:
> 1. Generation of finite difference coefficients. Again, one should be able to
> do this and get the results outside of the context of taking an actual
> derivative. Ideally, we could generate coefficients for any flavor (forward,
> central, backward) and order.
> 2. Selection of the bandwidth. This is, to be honest, the trickiest part of
> computing a numerical derivative. There is some "art" to picking a proper
> bandwidth that will generate an accurate numerical derivative - there are two
> competing sources of error (roundoff, due to the finite representation of
> floating points; and truncation, due to the inherent nature of finite
> differences). Ideally, we want to pick a bandwidth that will minimize the
> *total* error.
> 3. Actually computing the finite difference derivative estimate. This is
> really easy once you have 1. and 2.
> 4. Extend 1-3 to include support for multivariate finite differences.
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