[jira] [Closed] (SPARK-1682) Add gradient descent w/o sampling and RDA L1 updater

["Dong Wang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-1682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dong Wang closed SPARK-1682.
----------------------------
    Resolution: Later

revisit later

> Add gradient descent w/o sampling and RDA L1 updater
> ----------------------------------------------------
>
>                 Key: SPARK-1682
>                 URL: https://issues.apache.org/jira/browse/SPARK-1682
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.0.0
>            Reporter: Dong Wang
>
> The GradientDescent optimizer does sampling before a gradient step. When input data is already shuffled beforehand, it is possible to scan data and make gradient descent for each data instance. This could be potentially more efficient.
> Add enhanced RDA L1 updater, which could produce even sparse solutions with comparable quality compared with L1. Reference: 
> Lin Xiao, "Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization", Journal of Machine Learning Research 11 (2010) 2543-2596.
> Small fix: add options to BinaryClassification example to read and write model file



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