[jira] [Created] (FLINK-1965) Implement the Orthant-wise Limited Memory QuasiNewton optimization algorithm, a variant of L-BFGS that handles L1 regularization

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[jira] [Created] (FLINK-1965) Implement the Orthant-wise Limited Memory QuasiNewton optimization algorithm, a variant of L-BFGS that handles L1 regularization

Shang Yuanchun (Jira)
Theodore Vasiloudis created FLINK-1965:
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             Summary: 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


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/]




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