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Posted to issues@spark.apache.org by "Reza Zadeh (JIRA)" <ji...@apache.org> on 2015/03/16 21:18:39 UTC
[jira] [Updated] (SPARK-6346) Use faster converging optimization
method in MLlib
[ https://issues.apache.org/jira/browse/SPARK-6346?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reza Zadeh updated SPARK-6346:
------------------------------
Description:
According to experiments in SPARK-1503, the LBFGS algorithm converges much faster than our current proximal gradient, which is used throughout MLlib. This ticket is to track replacing slower-converging algorithms, with faster components e.g. LBFGS
This needs unification of the Optimization interface. For example, the LBFGS implementation should not know about RDDs.
was:According to experiments in SPARK-1503, the LBFGS algorithm converges much faster than our current proximal gradient, which is used throughout MLlib. This ticket is to track replacing slower-converging algorithms, with faster components e.g. LBFGS
> Use faster converging optimization method in MLlib
> --------------------------------------------------
>
> Key: SPARK-6346
> URL: https://issues.apache.org/jira/browse/SPARK-6346
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Reporter: Reza Zadeh
>
> According to experiments in SPARK-1503, the LBFGS algorithm converges much faster than our current proximal gradient, which is used throughout MLlib. This ticket is to track replacing slower-converging algorithms, with faster components e.g. LBFGS
> This needs unification of the Optimization interface. For example, the LBFGS implementation should not know about RDDs.
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