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Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
[article]
2018
arXiv
pre-print
Cubic regularization (CR) is an optimization method with emerging popularity due to its capability to escape saddle points and converge to second-order stationary solutions for nonconvex optimization. However, CR encounters a high sample complexity issue for finite-sum problems with a large data size. complexity. In this paper, we propose a stochastic variance-reduced cubic-regularization (SVRC) method under random sampling, and study its convergence guarantee as well as sample complexity. We
arXiv:1802.07372v2
fatcat:ftxphrtjirdoflxqfnr5v2q45a