Efficient Multilevel Eigensolvers with Applications to Data Analysis Tasks

By Dan Kushnir, Meirav Galun, Achi Brandt. 2010

31 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Numerical Methods & Scientific Computing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yoel Shkolnisky
2. Nir Sharon

In Electrical and Electronic Engineering

1. Yoel Shkolnisky
2. Nir Sharon

In Numerical Methods & Scientific Computing

1. Yoel Shkolnisky
2. Nir Sharon

Citations in the corpus

• An Adaptive Algebraic Multigrid Algorithm for Low-Rank Canonical Tensor Decomposition (2011, unknown): “Conversely, choosing too small a coarsest level may negatively impact convergence, or in some cases may even cause divergence (as in [21, 15, 31]).”
• Relaxation-based coarsening and multiscale graph organization (2010, unknown): “The fast way to calculate them should involve using a multiscale algorithm such as AMG (which is likely to work well in those cases where hierarchical organizations of the graph is meaningful; the AMG solver can, by the way, calculate many eigenvectors for nearly the same work of calculating only on”
• A class of Laplacian multiwavelets bases for high-dimensional data (2015, methodology): “A linear O(Nk) implementation of this procedure is suggested in [29] using multigrid approaches.”