Efficient coordinate-wise leading eigenvector computation

Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
; Proceedings of Algorithmic Learning Theory, PMLR 83:806-820, 2018.

Abstract

We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergence with overall runtime guarantees that are at least as good as Lanczos’s method and dominate it for slowly decaying spectrum. Our methods are based on combining a shift-and-invert approach with coordinate-wise algorithms for linear regression.

Cite this Paper


BibTeX
@InProceedings{pmlr-v83-wang18a, title = {Efficient coordinate-wise leading eigenvector computation}, author = {Jialei Wang and Weiran Wang and Dan Garber and Nathan Srebro}, booktitle = {Proceedings of Algorithmic Learning Theory}, pages = {806--820}, year = {2018}, editor = {Firdaus Janoos and Mehryar Mohri and Karthik Sridharan}, volume = {83}, series = {Proceedings of Machine Learning Research}, month = {07--09 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v83/wang18a/wang18a.pdf}, url = {http://proceedings.mlr.press/v83/wang18a.html}, abstract = {We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergence with overall runtime guarantees that are at least as good as Lanczos’s method and dominate it for slowly decaying spectrum. Our methods are based on combining a shift-and-invert approach with coordinate-wise algorithms for linear regression.} }
Endnote
%0 Conference Paper %T Efficient coordinate-wise leading eigenvector computation %A Jialei Wang %A Weiran Wang %A Dan Garber %A Nathan Srebro %B Proceedings of Algorithmic Learning Theory %C Proceedings of Machine Learning Research %D 2018 %E Firdaus Janoos %E Mehryar Mohri %E Karthik Sridharan %F pmlr-v83-wang18a %I PMLR %J Proceedings of Machine Learning Research %P 806--820 %U http://proceedings.mlr.press %V 83 %W PMLR %X We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergence with overall runtime guarantees that are at least as good as Lanczos’s method and dominate it for slowly decaying spectrum. Our methods are based on combining a shift-and-invert approach with coordinate-wise algorithms for linear regression.
APA
Wang, J., Wang, W., Garber, D. & Srebro, N.. (2018). Efficient coordinate-wise leading eigenvector computation. Proceedings of Algorithmic Learning Theory, in PMLR 83:806-820

Related Material