Stochastic Recursive Variance-Reduced Cubic Regularization Methods
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Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have received increasing attention due to its improved gradient/Hessian complexities (i.e., number of queries to stochastic gradient/Hessian oracles) to find local minima for nonconvex finite-sum optimization. However, it is unclear whether existing SVRC algorithms can be further improved. Moreover, the semi-stochastic Hessian estimator adopted in existing SVRC algorithms prevents the use of Hessian-vector product-based fast cubic subproblem solvers, which makes SVRC algorithms computationally intractable for high-dimensional problems. In this paper, we first present a Stochastic Recursive Variance-Reduced Cubic regularization method (SRVRC) using a recursively updated semi-stochastic gradient and Hessian estimators. It enjoys improved gradient and Hessian complexities to find an (ϵ, √(ϵ))-approximate local minimum, and outperforms the state-of-the-art SVRC algorithms. Built upon SRVRC, we further propose a Hessian-free SRVRC algorithm, namely SRVRC_free, which only requires stochastic gradient and Hessian-vector product computations, and achieves Õ(dnϵ^-2 dϵ^-3) runtime complexity, where n is the number of component functions in the finite-sum structure, d is the problem dimension, and ϵ is the optimization precision. This outperforms the best-known runtime complexity Õ(dϵ^-3.5) achieved by stochastic cubic regularization algorithm proposed in Tripuraneni et al. 2018.
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