A Riemannian Proximal Newton Method
将近端牛顿方法推广到嵌入子流形，用于求解含L1正则项的非光滑优化问题，证明了局部超线性收敛率，并给出全局收敛的混合版本。
In recent years, the proximal gradient method and its variants have been generalized to Riemannian manifolds for solving optimization problems with an additively separable structure, i.e., f+h, where f is continuously differentiable, and h may be nonsmooth but convex with computationally reasonable proximal mapping. In this paper, we generalize the proximal Newton method to embedded submanifolds for solving the type of problem with h(x)=μ∥x∥1. The generalization relies on the Weingarten and semismooth analysis. It is shown that the Riemannian proximal Newton method has a local superlinear convergence rate under certain reasonable assumptions. Moreover, a hybrid version is given by concatenating a Riemannian proximal gradient method and the Riemannian proximal Newton method. It is shown that if the objective function satisfies the Riemannian KL property and the switch parameter is chosen appropriately, then the hybrid method converges globally and also has a local superlinear convergence rate. Numerical experiments on random and synthetic data are used to demonstrate the performance of the proposed methods.