Review for NeurIPS paper: Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems

Additional Feedback: To summarize, the authors consider the problem of finding approximate Second Order Stationary Points (SOSPs). Compared with other works, the authors assume that the objective is generally non-convex and constrains are linear. Two methods are designed to solve this optimization problem. Both of these two methods are proved to have polynomial per-iteration complexity and global sublinear rates. In general, the problem is both theoretically and empirically interesting.