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https://issues.apache.org/jira/browse/SPARK-3382?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14600058#comment-14600058
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Joseph K. Bradley commented on SPARK-3382:
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[~lewuathe] I'm sorry for the iterative design, but I think this should be the
final request. Could you please modify your PR to following the discussion in
[SPARK-1503] for TFOCS? In summary:
* If the norm of the new weight vector is > 1, use relative tolerance
(normalizing by the norm of the new weight vector).
* If the norm of the new weight vector is <= 1, use absolute tolerance (not
normalizing).
If you prefer, I could also merge your PR and then send a follow-up
modification myself. I appreciate your patience, and I'm glad we're taking the
time to design this well!
> GradientDescent convergence tolerance
> -------------------------------------
>
> Key: SPARK-3382
> URL: https://issues.apache.org/jira/browse/SPARK-3382
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> GradientDescent should support a convergence tolerance setting. In general,
> for optimization, convergence tolerance should be preferred over a limit on
> the number of iterations since it is a somewhat data-adaptive or
> data-specific convergence criterion.
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