On inexact stochastic splitting methods for a class of nonconvex composite optimization problems with relative error

Author:

Hu Jia1,Han Congying12,Guo Tiande12,Zhao Tong12

Affiliation:

1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, People's Republic of China

2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, People's Republic of China

Funder

National key research and development program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Reference44 articles.

1. An inexact proximal generalized alternating direction method of multipliers

2. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods

3. R. Babanezhad, M.O. Ahmed, A. Virani, M. Schmidt, J. Konečn y`, and S. Sallinen, Stop wasting my gradients: Practical SVRG, in Advances in Neural Information Processing Systems, Curran Associates, Montréal, QC, 2015, pp. 2251–2259.

4. On Proximal Subgradient Splitting Method for Minimizing the sum of two Nonsmooth Convex Functions

5. A. Defazio, F. Bach, and S. Lacoste-Julien, SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives, in Advances in Neural Information Processing Systems, Curran Associates, Montréal, QC, 2014, pp. 1646–1654.

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