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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, 09 Mar 2008 10:35:46 GMT

    [ https://issues.apache.org/jira/browse/MATH-177?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12576745#action_12576745
] 

Luc Maisonobe commented on MATH-177:
------------------------------------

The estimation and optimization packages should really be redesigned. They are independent
despite they address similar problems.

A more general approach should be adopted, which could be specialized according to type of
function minimized (general versus quadratic forms) as proposed in this issue, but also according
to availability or not of gradient (which is the main difference between the estimation and
optimization packages).

Of course, this will introduce incompatible changes so can be done only for 2.0.

> 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
>
>
> 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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