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Preconditioned Parallel Block–Jacobi SVD Algorithm
"... We show experimentally, that the QR factorization with the complete column pivoting, optionally followed by the LQ factorization of the Rfactor, can lead to a substantial decrease of the number of outer parallel iteration steps in the parallel blockJacobi SVD algorithm, whereby the details depend ..."
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We show experimentally, that the QR factorization with the complete column pivoting, optionally followed by the LQ factorization of the Rfactor, can lead to a substantial decrease of the number of outer parallel iteration steps in the parallel blockJacobi SVD algorithm, whereby the details depend
PRECONDITIONING IN THE PARALLEL BLOCKJACOBI SVD ALGORITHM
 PROCEEDINGS OF ALGORITMY 2005 PP. 202–211
, 2005
"... One way, how to speed up the computation of the singular value decomposition of a given matrix A ∈ C m×n, m ≥ n, by the parallel twosided blockJacobi method, consists of applying some preprocessing steps that would concentrate the Frobenius norm near the diagonal. Such a concentration should hope ..."
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One way, how to speed up the computation of the singular value decomposition of a given matrix A ∈ C m×n, m ≥ n, by the parallel twosided blockJacobi method, consists of applying some preprocessing steps that would concentrate the Frobenius norm near the diagonal. Such a concentration should
ON DATA LAYOUT IN THE PARALLEL BLOCKJACOBI SVD ALGORITHM WITH PRE–PROCESSING ∗
"... Abstract. An efficient version of the parallel twosided blockJacobi algorithm for the singular value decomposition of an m × n matrix A includes the preprocessing step, which consists of the QR factorization of A with column pivoting followed by the optional LQ factorization of the Rfactor. Then ..."
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Abstract. An efficient version of the parallel twosided blockJacobi algorithm for the singular value decomposition of an m × n matrix A includes the preprocessing step, which consists of the QR factorization of A with column pivoting followed by the optional LQ factorization of the Rfactor
New CliqueBased Parallel Orderings for the Block–Jacobi EVD/SVD Algorithm
, 2006
"... We propose a new method for finding a parallel ordering needed in the parallel twosided blockJacobi EVD/SVD method. For a given matrix A, partitioned into block columns and block rows, such an ordering defines the subproblems that are solved in parallel in each parallel iteration step. Our appro ..."
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We propose a new method for finding a parallel ordering needed in the parallel twosided blockJacobi EVD/SVD method. For a given matrix A, partitioned into block columns and block rows, such an ordering defines the subproblems that are solved in parallel in each parallel iteration step. Our
An Analysis Of Parallel Implementations Of The BlockJacobi Algorithm For Computing The Svd
"... We analyze message passing communication model of distributed memory computers and show that, by using such model, we can imp lement onesided blockJacob i method for computing the singu lar value decomposition in a highly scalable manner. Key words: distributed memory, singular values, block algo ..."
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Cited by 2 (0 self)
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algorithms, Jacobi method 1 Introduction In this paper we first analyze the requirements to a communication environment in order to make scalable implementations of algorithms in numerical linear algebra possible. As a typical example of such an algorithm, we then investigate the behavior of the blockJacobi
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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signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the KSVD algorithm—generalizing the umeans clustering process. KSVD is an iterative method
Fast Parallel Algorithms for ShortRange Molecular Dynamics
 JOURNAL OF COMPUTATIONAL PHYSICS
, 1995
"... Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dyn ..."
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Cited by 622 (6 self)
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Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
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