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From "Gilles (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MATH-924) new multivariate vector optimizers cannot be used with large number of weights
Date Sat, 29 Dec 2012 02:22:12 GMT

     [ https://issues.apache.org/jira/browse/MATH-924?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Gilles updated MATH-924:
------------------------

    Attachment: MATH-924

Keeping the matrix concept, by representing uncorrelated observations with a diagonal weight
matrix (instead of an array), allows to solve this issue with minimal changes to the code:
# The optimizer's API is untouched.
# The possibility to have correlated observations is kept.

The attached patch contains a minimal "DiagonalMatrix" implementation, needing overview (ant
unit tests).

                
> new multivariate vector optimizers cannot be used with large number of weights
> ------------------------------------------------------------------------------
>
>                 Key: MATH-924
>                 URL: https://issues.apache.org/jira/browse/MATH-924
>             Project: Commons Math
>          Issue Type: Bug
>            Reporter: Luc Maisonobe
>            Priority: Critical
>             Fix For: 3.1.1
>
>         Attachments: MATH-924
>
>
> When using the Weigth class to pass a large number of weights to multivariate vector
optimizers, an nxn full matrix is created (and copied) when a n elements vector is used. This
exhausts memory when n is large.
> This happens for example when using curve fitters (even simple curve fitters like polynomial
ones for low degree) with large number of points. I encountered this with curve fitting on
41200 points, which created a matrix with 1.7 billion elements.

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