Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization

international conference on machine learning, 2019.

Cited by: 6|Bibtex|Views13
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Other Links: academic.microsoft.com|dblp.uni-trier.de|arxiv.org

Abstract:

In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential estimator (SPIDER), called as SPIDER-ADMM. Moreover, we prove that the SPIDER-ADMM achieves a record-breaking incremental first-order oracle (IFO) complexity o...More

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