منابع مشابه
Householder QR Factorization With Randomization for Column Pivoting (HQRRP)
A fundamental problem when adding column pivoting to the Householder QR factorization is that only about half of the computation can be cast in terms of high performing matrixmatrix multiplications, which greatly limits the benefits that can be derived from so-called blocking of algorithms. This paper describes a technique for selecting groups of pivot vectors by means of randomized projections...
متن کاملCommunication Avoiding Rank Revealing QR Factorization with Column Pivoting
In this paper we introduce CARRQR, a communication avoiding rank revealing QRfactorization with tournament pivoting. We show that CARRQR reveals the numerical rank of amatrix in an analogous way to QR factorization with column pivoting (QRCP). Although the upperbound of a quantity involved in the characterization of a rank revealing factorization is worse forCARRQR than for QRCP...
متن کاملA BLAS-3 Version of the QR Factorization with Column Pivoting
The QR factorization with column pivoting (QRP), originally suggested by Golub and Businger in 1965, is a popular approach to computing rank-revealing factorizations. Using BLAS Level 1, it was implemented in LINPACK, and, using BLAS Level 2, in LAPACK. While the BLAS Level 2 version delivers, in general, superior performance, it may result in worse performance for large matrix sizes due to cac...
متن کاملParallelization of the QR Decomposition with Column Pivoting Using Column Cyclic Distribution on Multicore and GPU Processors
The QR decomposition with column pivoting (QRP) of a matrix is widely used for rank revealing. The performance of LAPACK implementation (DGEQP3) of the Householder QRP algorithm is limited by Level 2 BLAS operations required for updating the column norms. In this paper, we propose an implementation of the QRP algorithm using a distribution of the matrix columns in a round-robin fashion for bett...
متن کاملFeature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification
Selection of features is one of the important tasks in the application like Speaker Identification (SI) and other pattern recognition problems. When multiple features are extracted from the same frame of speech, it is expected that a feature vector would contain redundant features. Redundant features confuse the speaker model in multidimensional space resulting in degraded performance by the sy...
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ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2017
ISSN: 1064-8275,1095-7197
DOI: 10.1137/15m1044680