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Date: Sun, 9 Mar 2008 03:35:46 -0700 (PDT)
From: "Luc Maisonobe (JIRA)" <jira@apache.org>
To: issues@commons.apache.org
Subject: [jira] Commented: (MATH-177) Provide a general minimizing package
 with a classical Gauss-Newton algorithm
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    [ 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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