2018
DOI: 10.1109/jstsp.2018.2867448
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Compressed Randomized UTV Decompositions for Low-Rank Matrix Approximations

Abstract: Low-rank matrix approximations play a fundamental role in numerical linear algebra and signal processing applications. This paper introduces a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique. CoR-UTV is primarily developed to compute an approximation to a low-rank input matrix by making use of random sampling schemes. Given a large and dense matrix of size m × n with numerical rank k, w… Show more

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Cited by 22 publications
(10 citation statements)
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“…Lastly, a truncated SVD follows to construct an approximate SVD of the given matrix. The work in [5] presented a rank-revealing algorithm using the randomized scheme termed compressed randomized UTV (CoR-UTV) decomposition. This algorithm is described in Algorithm 2.…”
Section: Outputmentioning
confidence: 99%
See 3 more Smart Citations
“…Lastly, a truncated SVD follows to construct an approximate SVD of the given matrix. The work in [5] presented a rank-revealing algorithm using the randomized scheme termed compressed randomized UTV (CoR-UTV) decomposition. This algorithm is described in Algorithm 2.…”
Section: Outputmentioning
confidence: 99%
“…The accuracy of SRQR can match that of CPQR. (For a comparison of CPQR, the SVD and randomized methods in terms of approximation accuracy, see, e.g., [20], [5].) The authors in [46] presented the Flip-Flop SRQR factorization algorithm.…”
Section: Outputmentioning
confidence: 99%
See 2 more Smart Citations
“…In this section, we present a randomized rank-revealing decomposition algorithm termed compressed randomized UTV (CoR-UTV) decomposition [44], which computes a low-rank approximation of a given matrix. We focus on the matrix A with m ≥ n, where CoR-UTV, in the form of (1), produces an upper triangular middle matrix T. The modifications required for a CoR-UTV for the case m < n that produces a lower triangular middle matrix T is straightforward.…”
Section: Compressed Randomized Utv Decompositionsmentioning
confidence: 99%