A Nonmonotone Trust Region Method for Unconstrained Optimization Problems on Riemannian Manifolds

We propose a nonmonotone trust region method for unconstrained optimization problems on Riemannian manifolds. Global convergence to the first-order stationary points is proved under some reasonable conditions. We also establish local R-linear, super-linear and quadratic convergence rates. Preliminary experiments show that the algorithm is efficient.

Automated summary

The paper introduces a nonmonotone trust region method for unconstrained optimization problems on Riemannian manifolds, showing global convergence to first-order stationary points and establishing local R-linear, super-linear, and quadratic convergence rates. Preliminary experiments suggest the algorithm's efficiency.