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Posted to issues@flink.apache.org by "Theodore Vasiloudis (JIRA)" <ji...@apache.org> on 2015/04/30 17:37:06 UTC
[jira] [Updated] (FLINK-1965) Implement the Orthant-wise Limited
Memory QuasiNewton optimization algorithm, a variant of L-BFGS that handles
L1 regularization
[ https://issues.apache.org/jira/browse/FLINK-1965?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Theodore Vasiloudis updated FLINK-1965:
---------------------------------------
Description:
The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton optimization method similar to L-BFGS that can handle L1 regularization.
Implementing this would allow us to obtain sparse solutions while at the same time having the convergence benefits of a quasi-Newton method, when compared to stochastic gradient descent.
[Link to paper|http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf]
[Link to example implementation|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
was:
The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton optimization method similar to L-BFGS that can handle L1 regularization.
Implementing this would allow us to obtain sparse solutions while at the same time having the convergence benefits of a quasi-Newton method, when compared to stochastic gradient descent.
[Link to paper|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
> Implement the Orthant-wise Limited Memory QuasiNewton optimization algorithm, a variant of L-BFGS that handles L1 regularization
> --------------------------------------------------------------------------------------------------------------------------------
>
> Key: FLINK-1965
> URL: https://issues.apache.org/jira/browse/FLINK-1965
> Project: Flink
> Issue Type: Wish
> Components: Machine Learning Library
> Reporter: Theodore Vasiloudis
> Priority: Minor
> Labels: ML
>
> The Orthant-wise Limited Memory QuasiNewton (OWL-QN) is a quasi-Newton optimization method similar to L-BFGS that can handle L1 regularization.
> Implementing this would allow us to obtain sparse solutions while at the same time having the convergence benefits of a quasi-Newton method, when compared to stochastic gradient descent.
> [Link to paper|http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf]
> [Link to example implementation|http://research.microsoft.com/en-us/downloads/b1eb1016-1738-4bd5-83a9-370c9d498a03/]
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