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From "Luc Maisonobe (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MATH-177) Provide a general minimizing package with a classical Gauss-Newton algorithm
Date Sun, 15 Mar 2009 21:54:50 GMT

    [ https://issues.apache.org/jira/browse/MATH-177?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12682181#action_12682181
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Luc Maisonobe commented on MATH-177:
------------------------------------

The redesigned optimization package is almost in place as of 2009-03-15.
The existing algorithms have been ported to the new API.
There is still some work to do to add new algorithms like conjugate gradient and steepest
descent.

> Provide a general minimizing package with a classical Gauss-Newton algorithm
> ----------------------------------------------------------------------------
>
>                 Key: MATH-177
>                 URL: https://issues.apache.org/jira/browse/MATH-177
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 2.0
>            Reporter: Mick
>            Assignee: Luc Maisonobe
>             Fix For: 2.0
>
>         Attachments: BrentMinimizer.java, UnivariateRealSolver.java, UnivariateRealSolverImpl.java
>
>
> Currently the math API provides least squares only method for minimizing (solving). The
limitation to least-squares problems comes from the Levenberg-Marquardt algorithm. A more
general minimizer (not for quadratic forms) could be implemented by refactoring this with
a classical GN, steepest descent and also conjugate gradient. We could use them as a basis
for some least-squares solvers (and also keep the very efficient and specialized Levenberg-Marquardt
too).
> Based on email exchange with Luc Maisonobe entitled [math] Minimizer on 1/15/08.

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