On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions
Data Structures and Algorithms
2014-11-05 v2 Numerical Analysis
Abstract
We describe two algorithms for computing a sparse solution to a least-squares problem where the coefficient matrix can have arbitrary dimensions. We show that the solution vector obtained by our algorithms is close to the solution vector obtained via the truncated SVD approach.
Keywords
Cite
@article{arxiv.1201.0073,
title = {On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions},
author = {Christos Boutsidis},
journal= {arXiv preprint arXiv:1201.0073},
year = {2014}
}
Comments
This paper has been withdrawn by the author. This article has been replaced by another submission: arXiv:1312.7499