2019
DOI: 10.1109/tnnls.2018.2889976
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Parallel Coordinate Descent Newton Method for Efficient $L_{1}$ -Regularized Loss Minimization

Abstract: The recent years have witnessed advances in parallel algorithms for large scale optimization problems. Notwithstanding the demonstrated success, existing algorithms that parallelize over features are usually limited by divergence issues under high parallelism or require data preprocessing to alleviate these problems. In this work, we propose a Parallel Coordinate Descent algorithm using approximate Newton steps (PCDN) that is guaranteed to converge globally without data preprocessing. The key component of the … Show more

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Cited by 5 publications
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