David A. Selby
IBM J. Res. Dev
We compute a sparse solution to the classical least-squares problem minx||Ax-b||2, where A is an arbitrary matrix. We describe a novel algorithm for this sparse least-squares problem. The algorithm operates as follows: first, it selects columns from A, and then solves a least-squares problem only with the selected columns. The column selection algorithm that we use is known to perform well for the well studied column subset selection problem. The contribution of this article is to show that it gives favorable results for sparse least-squares as well. Specifically, we prove that the solution vector obtained by our algorithm is close to the solution vector obtained via what is known as the "SVD-truncated regularization approach". © 2013 Elsevier B.V.
David A. Selby
IBM J. Res. Dev
Hang-Yip Liu, Steffen Schulze, et al.
Proceedings of SPIE - The International Society for Optical Engineering
Yigal Hoffner, Simon Field, et al.
EDOC 2004
György E. Révész
Theoretical Computer Science