A faster stochastic alternating direction method for large scale convex composite problems

Author:

Hu Jia,Guo Tiande,Zhao TongORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference49 articles.

1. Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60(2):223–311

2. Johnson R, Zhang T (2013) Accelerating stochastic gradient descent using predictive variance reduction. In: Advances in neural information processing systems, pp 315–323

3. Xiao L, Zhang T (2014) A proximal stochastic gradient method with progressive variance reduction. SIAM J Optim 24(4):2057–2075

4. Zheng S, Kwok JT (2016) Fast-and-light stochastic admm. In: International joint conference on artificial intelligence, pp 2407–2613

5. Konečnỳ J, Liu J, Richtárik P, Takáč M (2015) Mini-batch semi-stochastic gradient descent in the proximal setting. IEEE J Sel Top Signal Process 10(2):242–255

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