CRGD augments the objective with a penalty on negative Hessian eigenvalues to create a Lyapunov function guaranteeing convergence to second-order stationary points at user-selectable rates.
Cubic regularization of Newton method and its global performance,
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Saddle Point Evasion via Curvature-Regularized Gradient Dynamics
CRGD augments the objective with a penalty on negative Hessian eigenvalues to create a Lyapunov function guaranteeing convergence to second-order stationary points at user-selectable rates.