Presentation 2013-11-13
Stochastic Dual Coordinate Ascent with Alternating Direction Multiplier Method
Taiji SUZUKI,
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Abstract(in English) We propose a new stochastic dual coordinate ascent technique that can be applied to a wide range of regularized learning problems. Our method is based on Alternating Direction Multiplier Method (ADMM) to deal with complex regularization functions such as structured regularizations. Although the original ADMM is a batch method, the proposed method offers a stochastic update rule where each iteration requires only one or few sample observations. Moreover, our method can naturally afford mini-batch update and it gives speed up of convergence. We show that, under mild assumptions, our method converges exponentially. The numerical experiments show that our method actually performs efficiently.
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Keyword(in English) Stochastic Dual Coordinate Ascent / Alternating Direction Multiplier Method / Exponential Convergence / Structured Sparsity
Paper # IBISML2013-63
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Committee IBISML
Conference Date 2013/11/5(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Stochastic Dual Coordinate Ascent with Alternating Direction Multiplier Method
Sub Title (in English)
Keyword(1) Stochastic Dual Coordinate Ascent
Keyword(2) Alternating Direction Multiplier Method
Keyword(3) Exponential Convergence
Keyword(4) Structured Sparsity
1st Author's Name Taiji SUZUKI
1st Author's Affiliation Department of Mathematical and Computing Sciences()
Date 2013-11-13
Paper # IBISML2013-63
Volume (vol) vol.113
Number (no) 286
Page pp.pp.-
#Pages 8
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