Results 1  10
of
2,588
CIMGS: An Incomplete Orthogonal Factorization
, 1993
"... A new preconditioner (called CIMGS) based on an incomplete orthogonal factorization is derived, analyzed, and tested. Although designed for preconditioning least squares problems, it is also applicable to more general symmetric positive definite matrices. CIMGS is robust both theoretically and empir ..."
Abstract
 Add to MetaCart
A new preconditioner (called CIMGS) based on an incomplete orthogonal factorization is derived, analyzed, and tested. Although designed for preconditioning least squares problems, it is also applicable to more general symmetric positive definite matrices. CIMGS is robust both theoretically
Computing sparse orthogonal factors in MATLAB
, 1998
"... In this report a new version of the multifrontal sparse QR factorization routine sqr, originally by Matstoms, for general sparse matrices is described and evaluated. In the previous version the orthogonal factor Q is discarded due to storage considerations. The new version provides Q and uses the mu ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
In this report a new version of the multifrontal sparse QR factorization routine sqr, originally by Matstoms, for general sparse matrices is described and evaluated. In the previous version the orthogonal factor Q is discarded due to storage considerations. The new version provides Q and uses
CIMGS: An incomplete orthogonal factorization preconditioner
 SIAM J. Sci. Comput
, 1997
"... Abstract. A new preconditioner for symmetric positive definite systems is proposed, analyzed, and tested. The preconditioner, compressed incomplete modified Gram–Schmidt (CIMGS), is based on an incomplete orthogonal factorization. CIMGS is robust both theoretically and empirically, existing (in exac ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
Abstract. A new preconditioner for symmetric positive definite systems is proposed, analyzed, and tested. The preconditioner, compressed incomplete modified Gram–Schmidt (CIMGS), is based on an incomplete orthogonal factorization. CIMGS is robust both theoretically and empirically, existing (in
Characterizing the orthogonality factor in WCDMA downlinks
 IEEE Trans. Wireless Commun
, 2003
"... Multipath dispersion leads to the loss of orthogonality between signals transmitted simultaneously on a wideband code division multiple access (WCDMA) downlink. The orthogonality factor (OF), which models its impact in the link signaltointerferenceplusnoise ratio (SINR) equation, dependsto a la ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
Multipath dispersion leads to the loss of orthogonality between signals transmitted simultaneously on a wideband code division multiple access (WCDMA) downlink. The orthogonality factor (OF), which models its impact in the link signaltointerferenceplusnoise ratio (SINR) equation, dependsto a
A Multivariate Generalized Orthogonal Factor
, 2005
"... öMmföäflsäafaäsflassflassflas ffffffffffffffffffffffffffffffffffff ..."
Indexing by latent semantic analysis
 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
, 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higherorder structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
Abstract

Cited by 3779 (35 self)
 Add to MetaCart
. The particular technique used is singularvalue decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries
Updating and downdating an upper trapezoidal sparse orthogonal factorization
"... We describe how to update and downdate an upper trapezoidal sparse orthogonal factorization, namely the sparse QR factorization of AT k, where Ak is a “tall and thin” full column rank matrix formed with a subset of the columns of a fixed matrix A. In order to do that, we have adapted to rectangular ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
We describe how to update and downdate an upper trapezoidal sparse orthogonal factorization, namely the sparse QR factorization of AT k, where Ak is a “tall and thin” full column rank matrix formed with a subset of the columns of a fixed matrix A. In order to do that, we have adapted to rectangular
Incomplete Orthogonal Factorization using Givens Rotations
, 2013
"... InthispaperanapproachforfindingasparseincompleteCholeskyfactorthrough an incomplete orthogonal factorization with Givens rotations is discussed and applied to Gaussian Markov random fields (GMRFs). The incomplete Cholesky factor obtained from the incomplete orthogonal factorization is usually sparse ..."
Abstract
 Add to MetaCart
InthispaperanapproachforfindingasparseincompleteCholeskyfactorthrough an incomplete orthogonal factorization with Givens rotations is discussed and applied to Gaussian Markov random fields (GMRFs). The incomplete Cholesky factor obtained from the incomplete orthogonal factorization is usually
Greed is Good: Algorithmic Results for Sparse Approximation
, 2004
"... This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal representa ..."
Abstract

Cited by 916 (9 self)
 Add to MetaCart
This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal
Distributed Sparse Gaussian Elimination And Orthogonal Factorization
 LAPACK WORKING NOTE 64 (UT CS93203)
, 1993
"... We consider the solution of a linear system Ax = b on a distributed memory machine when the matrix A has full rank and is large, sparse and nonsymmetric. We use our Cartesian nested dissection algorithm to compute a fillreducing column ordering of the matrix. We develop algorithms that use the asso ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
are applied to compute the triangular factor of an orthogonal factorization. We compare the fill incurred by our approach to that incurred by well known sequential methods and report on the performance of our implementation on the Intel iPSC/860.
Results 1  10
of
2,588