1 citations · 1 across the 3 of their papers we have counts for
3 papers
math.OC2024
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
David Martínez-Rubio, Christophe Roux, Sebastian Pokutta
In this work, we analyze two of the most fundamental algorithms in geodesically convex optimization: Riemannian gradient descent and (possibly inexact) Riemannian proximal point. W…
math.OC2023
Open Problem: Polynomial linearly-convergent method for geodesically convex optimization?
Christopher Criscitiello, David Martínez-Rubio, Nicolas Boumal
Let be a Lipschitz and geodesically convex function defined on a -dimensional Riemannian manifold . Does there exist a first-o…
math.OC2023★ 1 cited
Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
David Martínez-Rubio, Elias Wirth, Sebastian Pokutta
It has recently been shown that ISTA, an unaccelerated optimization method, presents sparse updates for the -regularized personalized PageRank problem, leading to cheap ite…