Strong Rank Revealing QR decomposition

Version 1.0.3 (12,9 KB) von Xin Xing
A matlab implementation of the strong rank revealing QR.
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Aktualisiert 16. Jul 2021

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This code is for the strong rank-revealing QR (Strong RRQR) introduced in the following reference.
Reference:
Gu, Ming, and Stanley C. Eisenstat. "Efficient algorithms for computing a strong rank-revealing QR factorization." SIAM Journal on Scientific Computing 17.4 (1996): 848-869.
Given a target matrix A, a strong RRQR of A is of form
AP = [Q1, Q2] * [R11, R12; 0 R22] \approx Q1 [R11, R12]
where P is a permutation matrix, [Q1,Q2] has orthonormal columns, and R11 is an upper-triangular matrix. R11 and R12 need to satisfy that matrix inv(R11)*R12 has each entry bounded by a pre-specified constant that is not less than 1.
The code contains the strong rank revealing QR with (1) a fixed rank, i.e., the dimension of R11; (2) an error threshold, i.e., each column of R22 has norm less than the threshold. The implementation strictly follows the algorithms proposed in the above reference.
Note:
The main time-consuming step in the code is usually a complete pivoting QR decomposition of the target matrix A, i.e. [Q,R,p] = qr(A, 0). Practically, the algorithm only requires a partial pivoting QR (R22 doesn't have to be upper-triangular) but I can't find an efficient partial pivoting QR implementation. Greatly appreciated if any one can provide an efficient partial pivoting QR code for both a fixed rank or a fixed threshold.

Zitieren als

Xin Xing (2025). Strong Rank Revealing QR decomposition (https://de.mathworks.com/matlabcentral/fileexchange/69139-strong-rank-revealing-qr-decomposition), MATLAB Central File Exchange. Abgerufen.

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Erstellt mit R2016a
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StrongRRQR/StrongRRQR

Version Veröffentlicht Versionshinweise
1.0.3

Adding a m file for givens

1.0.2

*Fix a minor bug for SRRQR with rank being the number of columns.

1.0.1

Changed typos and added more descriptions.

1.0.0