36 citations · 61 across the 23 of their papers we have counts for
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Local convergence of alternating low-rank optimization methods with overrelaxation
Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew
The local convergence of alternating optimization methods with overrelaxation for low-rank matrix and tensor problems is established. The analysis is based on the linearization of…
Maximum relative distance between real rank-two and rank-one tensors
Henrik Eisenmann, André Uschmajew
It is shown that the relative distance in Frobenius norm of a real symmetric order- tensor of rank two to its best rank-one approximation is upper bounded by $\sqrt{1-(1-1/d)^{d…
A note on the optimal convergence rate of descent methods with fixed step sizes for smooth strongly convex functions
André Uschmajew, Bart Vandereycken
Based on a result by Taylor, Hendrickx, and Glineur (J. Optim. Theory Appl., 178(2):455--476, 2018) on the attainable convergence rate of gradient descent for smooth and strongly c…
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
Henrik Eisenmann, Felix Krahmer, Max Pfeffer +1
In this paper, we present modifications of the iterative hard thresholding (IHT) method for recovery of jointly row-sparse and low-rank matrices. In particular a Riemannian version…