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Comparative Analysis of Sparse Matrix Algorithms for Information Retrieval
"... We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR) application. We show the results of our impl ..."
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Cited by 6 (0 self)
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We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR) application. We show the results of our
GDAGsim: Sparse matrix algorithms for Bayesian computation
"... GDAGsim is a software library which can be used to carry out conditional sampling of linear Gaussian directed acyclic graph models, and hence can be used for the implementation of efficient block MCMC samplers for such models. This paper examines the software library and its design, and how it can b ..."
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GDAGsim is a software library which can be used to carry out conditional sampling of linear Gaussian directed acyclic graph models, and hence can be used for the implementation of efficient block MCMC samplers for such models. This paper examines the software library and its design, and how it can be applied to problems in Bayesian inference. 1
An Approach for Parallelizing any General Unsymmetric Sparse Matrix Algorithm
, 1995
"... In many large scale scientific and engineering computations, the solution to a sparse linear system is required. We present a partial unsymmetric nested dissection method that can be used to parallelize any general unsymmetric sparse matrix algorithm whose pivot search can be restricted to a subset ..."
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Cited by 2 (0 self)
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In many large scale scientific and engineering computations, the solution to a sparse linear system is required. We present a partial unsymmetric nested dissection method that can be used to parallelize any general unsymmetric sparse matrix algorithm whose pivot search can be restricted to a subset
The University of Florida sparse matrix collection
 NA DIGEST
, 1997
"... The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural enginee ..."
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Cited by 538 (19 self)
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and graphs, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, and power networks). The collection meets a vital need that artificiallygenerated matrices cannot meet, and is widely used by the sparse matrix algorithms community
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
 ACM Trans. Math. Software
, 1982
"... An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerica ..."
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Cited by 649 (21 self)
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An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable
IIMS Postgraduate Seminar 2009 Parallel sparse matrix algorithms for numerical computing Matrixvector multiplication
"... Matrix computing has played an important part in numeric computing. Sparse matrix is a type of matrix that used in all kinds of computing, and sparse matrix computing has significant meaning in the different fields. Sparse Matrix computing includes so many different operations, for example, addition ..."
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. This report focuses on how to implement the sparse matrix vector multiplication using parallel in detail; it will base on sparse matrix’s low computation and higher communication characteristics under parallel computation and will introduce a simple algorithm with parallel computing, and display the results
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
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Cited by 1230 (5 self)
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Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
, 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signalatoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
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Cited by 930 (41 self)
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by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 539 (20 self)
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remarkable features making this attractive for lowrank matrix completion problems. The first is that the softthresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {X k} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal
Nonnegative matrix factorization with sparseness constraints
 Jour. of
, 2004
"... www.cs.helsinki.fi/patrik.hoyer ..."
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