From the 1 of 10 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
10 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…
A modified Riemannian Levenberg-Marquardt Algorithm for robust or constraint optimization on manifolds
Mateusz Baran, Ronny Bergmann
We extend the Levenberg-Marquardt method on Riemannian manifolds to a robust variant that allows to tackle problems from applications where outliers are to be expected. We formally…
A Riemannian quasi-Newton algorithm for optimization with Euclidean bounds
Mateusz Baran, Ronny Bergmann, Patryk Przybysz
We propose a Riemannian limited-memory BFGS method for optimization problems with Euclidean bounds. The method combines a limited-memory quasi-Newton update in the tangent space wi…
p-Laplacians for Manifold-valued Hypergraphs
Jo Andersson Stokke, Ronny Bergmann, Martin Hanik +1
Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we d…
Two Models for Surface Segmentation using the Total Variation of the Normal Vector
Manuel WeiÃ, Lukas Baumgärtner, Laura Weigl +3
We consider the problem of surface segmentation, where the goal is to partition a surface represented by a triangular mesh. The segmentation is based on the similarity of the norma…
Total Generalized Variation of the Normal Vector Field and Applications to Mesh Denoising
Lukas Baumgärtner, Ronny Bergmann, Roland Herzog +2
We propose a novel formulation for the second-order total generalized variation (TGV) of the normal vector on an oriented, triangular mesh embedded in . The normal vector is…