From the 1 of 5 linked papers with an AI index.
5 papers
The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization
Ronny Bergmann, Hajg Jasa, Paula John +1
The paper proposes an intrinsic Riemannian proximal gradient algorithm that operates directly on manifolds without requiring an embedding, and analyzes its convergence for possibly…
Riemannian Metric Preconditioning for Trajectory Tracking
Jacob R. Goodman, Hajg Jasa
We introduce a rank-one Riemannian cometric update inducing a modification of the Riemannian metric that makes specific directions of motion cheaper to travel along. We establish b…
Procrustes Problems on Random Matrices
Hajg Jasa, Ronny Bergmann, Christian Kümmerle +2
Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those…
The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
Ronny Bergmann, Hajg Jasa, Paula John +1
We consider a class of (possibly strongly) geodesically convex optimization problems on Hadamard manifolds, where the objective function splits into the sum of a smooth and a possi…
The Riemannian Convex Bundle Method
Ronny Bergmann, Roland Herzog, Hajg Jasa
We introduce the convex bundle method to solve convex, non-smooth optimization problems on Riemannian manifolds of bounded sectional curvature. Each step of our method is based on…