3 papers
math.NA2023
From low-rank retractions to dynamical low-rank approximation and back
Axel Séguin, Gianluca Ceruti, Daniel Kressner
In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More rec…
math.NA2022
Hermite interpolation with retractions on manifolds
Axel Séguin, Daniel Kressner
Interpolation of data on non-Euclidean spaces is an active research area fostered by its numerous applications. This work considers the Hermite interpolation problem: finding a suf…
math.OC2021
Continuation methods for Riemannian Optimization
Axel Séguin, Daniel Kressner
Numerical continuation in the context of optimization can be used to mitigate convergence issues due to a poor initial guess. In this work, we extend this idea to Riemannian optimi…