STOCHASTIC ALTERNATING DIRECTION METHOD OF MULTIPLIERS FOR STRUCTURED REGULARIZATION
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- Suzuki Taiji
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology PRESTO, Japan Science and Technology Agency, JAPAN
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<p>In this paper, we present stochastic optimization variants of the alternating direction method of multipliers (ADMM). ADMM is a useful method to solve a regularized risk minimization problem where the regularization term is complicated and not easily dealt with in an ordinary manner. For example, structured regularization is one of the typical applications of such regularization in which ADMM is effective. It includes group lasso regularization, low rank tensor regularization, and fused lasso regularization. Since ADMM is a general method and has wide applications, it is intensively studied and refined these days. However, ADMM is not suited to optimization problems with huge data. To resolve this problem, online stochastic optimization variants and a batch stochastic optimization variant of ADMM are presented. All the presented methods can be easily implemented and have wide applications. Moreover, the theoretical guarantees of the methods are given.</p>
収録刊行物
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- Journal of the Japanese Society of Computational Statistics
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Journal of the Japanese Society of Computational Statistics 28 (1), 105-124, 2015
日本計算機統計学会
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詳細情報 詳細情報について
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- CRID
- 1390001204413707904
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- NII論文ID
- 130005434004
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- NII書誌ID
- AA10823693
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- ISSN
- 18811337
- 09152350
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- NDL書誌ID
- 030633018
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- 使用不可