16 citations · 19 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Newton's method for nonlinear mappings into vector bundles Part II: Application to variational problems
Laura Weigl, Ronny Bergmann, Anton Schiela
We consider the solution of variational equations on manifolds by Newton's method. These problems can be expressed as root finding problems for mappings from infinite dimensional m…
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…
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…
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…
The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization
Ronny Bergmann, Hajg Jasa, Paula John +1
We consider the proximal gradient method on Riemannian manifolds for functions that are possibly not geodesically convex. Starting from the forward-backward-splitting, we define an…