Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
This addresses a fundamental challenge in machine learning for training nonconvex models, offering a faster and more stable approach to avoid saddle points, though it is incremental as it builds on existing stochastic gradient methods.
The paper tackles the problem of nonconvex optimization where first-order methods can get stuck at saddle points, proposing a method that escapes strict saddle points and converges to a second-order stationary point with a dimension-independent rate of Õ(ε⁻² log(1/ε)).
Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonconvex optimization. It has been observed that a stochastic gradient descent (SGD) step introduces anistropic noise around saddle points for deep learning and non-convex half space learning problems, which indicates that SGD satisfies the correlated negative curvature (CNC) condition for these problems. Therefore, we propose to use a separate SGD step to help the SCSG method escape from strict saddle points, resulting in the CNC-SCSG method. The SGD step plays a role similar to noise injection but is more stable. We prove that the resultant algorithm converges to a second-order stationary point with a convergence rate of $\tilde{O}( ε^{-2} log( 1/ε))$ where $ε$ is the pre-specified error tolerance. This convergence rate is independent of the problem dimension, and is faster than that of CNC-SGD. A more general framework is further designed to incorporate the proposed CNC-SCSG into any first-order method for the method to escape saddle points. Simulation studies illustrate that the proposed algorithm can escape saddle points in much fewer epochs than the gradient descent methods perturbed by either noise injection or a SGD step.