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https://issues.apache.org/jira/browse/SPARK-1542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Shuo Xiang closed SPARK-1542.
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Resolution: Duplicate
Duplicate with SPARK-1543 by mistake.
> Add ADMM for solving Lasso (and elastic net) problem
> ----------------------------------------------------
>
> Key: SPARK-1542
> URL: https://issues.apache.org/jira/browse/SPARK-1542
> Project: Spark
> Issue Type: New Feature
> Reporter: Shuo Xiang
> Priority: Minor
> Labels: features
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> This PR introduces the Alternating Direction Method of Multipliers (ADMM) for
> solving Lasso (elastic net, in fact) in mllib.
> ADMM is capable of solving a class of composite minimization problems in a
> distributed way. Specifically for Lasso (if only L1-regularization) or
> elastic-net (both L1- and L2- regularization), it requires solving
> independent systems of linear equations on each partition and a
> soft-threholding operation on the driver. Unlike SGD, it is a deterministic
> algorithm (except for the random partition). Details can be found in the [S.
> Boyd's paper](http://www.stanford.edu/~boyd/papers/admm_distr_stats.html).
> The linear algebra operations mainly rely on the Breeze library,
> particularly, it applies `breeze.linalg.cholesky` to perform cholesky
> decomposition on each partition to solve the linear system.
> I tried to follow the organization of existing Lasso implementation. However,
> as ADMM is also a good fit for similar optimization problems, e.g., (sparse)
> logistic regression, it may worth to re-organize and put ADMM into a separate
> section.
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