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Identification Using QR Decompositions?
, 2014
"... Multiorder covariance computation for estimates in stochastic subspace identification using QR decompositions ..."
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Multiorder covariance computation for estimates in stochastic subspace identification using QR decompositions
QR decomposition on GPUs
 In Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (Washington, D.C., March 08  08, 2009). GPGPU2
"... QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least squares problems. Performance of QR decomposition i ..."
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Cited by 13 (0 self)
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QR decomposition is a computationally intensive linear algebra operation that factors a matrix A into the product of a unitary matrix Q and upper triangular matrix R. Adaptive systems commonly employ QR decomposition to solve overdetermined least squares problems. Performance of QR decomposition
Communicationavoiding QR decomposition for
 GPU,” GPU Technology Conference, Research Poster A01
, 2010
"... Abstract—We describe an implementation of the CommunicationAvoiding QR (CAQR) factorization that runs entirely on a single graphics processor (GPU). We show that the reduction in memory traffic provided by CAQR allows us to outperform existing parallel GPU implementations of QR for a large class of ..."
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Cited by 19 (2 self)
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GPU QR factorization tuned specifically for that matrix size, and 30x faster than if we use Intel’s Math Kernel Library (MKL) singular value decomposition routine on a multicore
Virtual Systolic Array for QR Decomposition
"... Abstract—Systolic arrays offer a very attractive, datacentric, execution model as an alternative to the von Neumann architecture. Hardware implementations of systolic arrays turned out not to be viable solutions in the past. This article shows how the systolic design principles can be applied to a s ..."
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Cited by 4 (1 self)
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software solution to deliver an algorithm with unprecedented strong scaling capabilities. Systolic array for the QR decomposition is developed and a virtualization layer is used for mapping of the algorithm to a large distributed memory system. Strong scaling properties are discovered, superior to existing
Generalizations Of The Singular Value And QR Decomposition
 SIAM MATR. ANAL. & APPLIC
, 1992
"... In this paper, we discuss multimatrix generalizations of two wellknown orthogonal rank factorizations of a matrix: the generalized singular value decomposition and the generalized QR(or URV) decomposition. These generalizations can be obtained for any number of matrices of compatible dimensions ..."
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Cited by 15 (3 self)
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In this paper, we discuss multimatrix generalizations of two wellknown orthogonal rank factorizations of a matrix: the generalized singular value decomposition and the generalized QR(or URV) decomposition. These generalizations can be obtained for any number of matrices of compatible
Design of Systolic arrays for QR Decomposition
"... In this paper we propose design of three systolic arrays to perform QR decomposition of a square matrix. Our first design is based on Given’s rotation method [1]. In contrast to the earlier designs [1], [2] based on this method, our design uses n(n − 1)/2 homogeneous PEs. Our next design is based on ..."
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In this paper we propose design of three systolic arrays to perform QR decomposition of a square matrix. Our first design is based on Given’s rotation method [1]. In contrast to the earlier designs [1], [2] based on this method, our design uses n(n − 1)/2 homogeneous PEs. Our next design is based
Load Balanced Parallel QR Decomposition
"... This paper introduces a new parallel QR decomposition algorithm. The novel load balancing method described here considers total computational work as opposed to just balancing Givens rotations. This results in expected efficiencies which approach optimal as problem size grows relative to number of p ..."
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This paper introduces a new parallel QR decomposition algorithm. The novel load balancing method described here considers total computational work as opposed to just balancing Givens rotations. This results in expected efficiencies which approach optimal as problem size grows relative to number
A QRDecomposition for Matrix Pencils
 BIT
, 1999
"... This paper describes an efficient and numerically stable modification of the QR decomposition for solving a parametric set of linear least squares problems with a parametric matrix A+B for several values of the parameter . The method is demonstrated on a typical application. Keywords: linear least s ..."
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This paper describes an efficient and numerically stable modification of the QR decomposition for solving a parametric set of linear least squares problems with a parametric matrix A+B for several values of the parameter . The method is demonstrated on a typical application. Keywords: linear least
AN ALGORITHM FOR COMPUTING THE QR DECOMPOSITION OF A POLYNOMIAL MATRIX
"... algorithm for computing the QR decomposition of a polynomial matrix This item was submitted to Loughborough University's Institutional Repository by the/an author. ..."
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algorithm for computing the QR decomposition of a polynomial matrix This item was submitted to Loughborough University's Institutional Repository by the/an author.
The singular value decomposition and the QR decomposition in the extended max algebra
, 1995
"... The singular value decomposition and the QR decomposition in the extended max algebra ∗ ..."
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Cited by 1 (1 self)
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The singular value decomposition and the QR decomposition in the extended max algebra ∗
Results 1  10
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