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https://issues.apache.org/jira/browse/FLINK-1994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15110390#comment-15110390
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ASF GitHub Bot commented on FLINK-1994:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1397#discussion_r50382898
--- Diff: docs/libs/ml/optimization.md ---
@@ -256,6 +271,79 @@ The full list of supported prediction functions can be
found [here](#prediction-
</tbody>
</table>
+#### Effective Learning Rate ##
+
+Where:
+- $j$ is the iteration number
+- $\eta_j$ is the step size on step $j$
+- $\eta_0$ is the initial step size
+- $\lambda$ is the regularization constant
+- $k$ is the decay constant
--- End diff --
In which formula is `k` actually used?
> Add different gain calculation schemes to SGD
> ---------------------------------------------
>
> Key: FLINK-1994
> URL: https://issues.apache.org/jira/browse/FLINK-1994
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Trevor Grant
> Priority: Minor
> Labels: ML, Starter
>
> The current SGD implementation uses as gain for the weight updates the
> formula {{stepsize/sqrt(iterationNumber)}}. It would be good to make the gain
> calculation configurable and to provide different strategies for that. For
> example:
> * stepsize/(1 + iterationNumber)
> * stepsize*(1 + regularization * stepsize * iterationNumber)^(-3/4)
> See also how to properly select the gains [1].
> Resources:
> [1] http://arxiv.org/pdf/1107.2490.pdf
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