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https://issues.apache.org/jira/browse/SPARK-5256?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14486196#comment-14486196
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Joseph K. Bradley commented on SPARK-5256:
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*Q*: Do we like the "Updater" concept?
*Proposal*: No. It conflates the regularization type with the
regularization-related update. The regularization type should be a model
parameter. The update function should depend on the model's regularization
type and the optimizer. There are only two such update functions we need
currently: (sub)gradient step (for L1 or L2) and projection (for L1). We could
add more later.
> Improving MLlib optimization APIs
> ---------------------------------
>
> Key: SPARK-5256
> URL: https://issues.apache.org/jira/browse/SPARK-5256
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> *Goal*: Improve APIs for optimization
> *Motivation*: There have been several disjoint mentions of improving the
> optimization APIs to make them more pluggable, extensible, etc. This JIRA is
> a place to discuss what API changes are necessary for the long term, and to
> provide links to other relevant JIRAs.
> Eventually, I hope this leads to a design doc outlining:
> * current issues
> * requirements such as supporting many types of objective functions,
> optimization algorithms, and parameters to those algorithms
> * ideal API
> * breakdown of smaller JIRAs needed to achieve that API
> I will soon create an initial design doc, and I will try to watch this JIRA
> and include ideas from JIRA comments.
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