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On Rank-Revealing Factorisations
SIAM Journal on Matrix Analysis and Applications
Computing rank-revealing QR factorizations of dense matrices
ACM Transactions on Mathematical Software (TOMS)
Algorithm 853: An efficient algorithm for solving rank-deficient least squares problems
ACM Transactions on Mathematical Software (TOMS)
Unsupervised feature selection for principal components analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustered subset selection and its applications on it service metrics
Proceedings of the 17th ACM conference on Information and knowledge management
Randomized Algorithms for Matrices and Data
Foundations and Trends® in Machine Learning
Simple varieties for limited precision points
Theoretical Computer Science
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This article describes a suite of codes as well as associated testing and timing drivers for computing rank-revealing QR (RRQR) factorizations of dense matrices. The main contribution is an efficient block algorithm for approximating an RRQR factorization, employing a windowed version of the commonly used Golub pivoting strategy and improved versions of the RRQR algorithms for triangular matrices orginally suggersted by Chandrasekaran and Ipsen and by Pan and Tang, respectively, We highlight usage and features of these codes.