William Hinsberg, Joy Cheng, et al.
SPIE Advanced Lithography 2010
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.
William Hinsberg, Joy Cheng, et al.
SPIE Advanced Lithography 2010
Beomseok Nam, Henrique Andrade, et al.
ACM/IEEE SC 2006
Donald Samuels, Ian Stobert
SPIE Photomask Technology + EUV Lithography 2007
Qing Li, Zhigang Deng, et al.
IEEE T-MI