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From "Sebastian Schelter (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (GIRAPH-480) Add convergence detection to org.apache.giraph.examples.RandomWalkVertex
Date Tue, 15 Jan 2013 13:44:13 GMT

     [ https://issues.apache.org/jira/browse/GIRAPH-480?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sebastian Schelter updated GIRAPH-480:
--------------------------------------

    Description: 
I propose to add convergence detection to the RandomWalkVertex. Convergence is achieved when
the overall absolute change (L1 norm) of the difference between the current and the previous
probability vector becomes less than a given threshold. Convergence detection can be implemented
via an additional aggregator and a check in the master compute function.

This change would make the class much easier to use as the users don't have to worry about
the number of supersteps to execute, but can simply specify a high number as MAX_SUPERSTEPS
and be sure that the algorithm convergences when acceptable quality of the result is reached.

  was:
I propose to add convergence detection to the RandomWalkVertex. Convergence is achieved when
the overall absolute change (L1 norm) of the difference between the current and the previous
probability vector. Convergence detection can be implemented via an additional aggregator.

This change would make the class much easier to use as the users don't have to worry about
the number of supersteps, but can simply specify a high number as MAX_SUPERSTEPS and be sure
that the algorithm convergences when acceptable quality of the result is reached.

    
> Add convergence detection to org.apache.giraph.examples.RandomWalkVertex
> ------------------------------------------------------------------------
>
>                 Key: GIRAPH-480
>                 URL: https://issues.apache.org/jira/browse/GIRAPH-480
>             Project: Giraph
>          Issue Type: Improvement
>          Components: examples
>    Affects Versions: 0.2.0
>            Reporter: Sebastian Schelter
>            Assignee: Sebastian Schelter
>
> I propose to add convergence detection to the RandomWalkVertex. Convergence is achieved
when the overall absolute change (L1 norm) of the difference between the current and the previous
probability vector becomes less than a given threshold. Convergence detection can be implemented
via an additional aggregator and a check in the master compute function.
> This change would make the class much easier to use as the users don't have to worry
about the number of supersteps to execute, but can simply specify a high number as MAX_SUPERSTEPS
and be sure that the algorithm convergences when acceptable quality of the result is reached.

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