An Inexact Semi-smooth Newton Method on Riemannian Manifolds with Application to Duality-based Total Variation Denoising

Abstract

We propose a higher-order method for solving non-smooth optimization problems on manifolds. In order to obtain superlinear convergence, we apply a Riemannian Semi-smooth Newton method to a non-smooth non-linear primal-dual optimality system based on a recent extension of Fenchel duality theory to Riemannian manifolds. We also propose an inexact version of the Riemannian Semi-smooth Newton method and prove conditions for local linear and superlinear convergence that hold independent of the sign of the curvature. Numerical experiments on l2-TV-like problems with dual regularization confirm superlinear convergence on manifolds with positive and negative curvature.

References in corpus (4)

• Introduction to Nonsmooth Analysis and Optimization
• First- and Second-Order Analysis for Optimization Problems with Manifold-Valued Constraints
• Fenchel Duality Theory and A Primal-Dual Algorithm on Riemannian Manifolds
• Fixed Point Algorithm Based on Quasi-Newton Method for Convex Minimization Problem with Application to Image Deblurring

Cited by in corpus (1)

• Regularising orientation estimation in Cryo-EM 3D map refinement through measure-based lifting over Riemannian manifolds