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442
Discrete Choice Methods with Simulation
, 2002
"... This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logi ..."
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Cited by 1326 (20 self)
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This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered
Parallelization of the QR Decomposition with Column Pivoting Using Column Cyclic Distribution on Multicore and GPU Processors
"... Abstract. The QR decomposition with column pivoting (QRP) of a matrix is widely used for numerical rank revealing in applications. 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 ..."
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Abstract. The QR decomposition with column pivoting (QRP) of a matrix is widely used for numerical rank revealing in applications. The performance of LAPACK implementation (DGEQP3) of the Householder QRP algorithm is limited by Level 2 BLAS operations required for updating the column norms
Numerical methods for computing angles between linear subspaces
, 1971
"... Assume that two subspaces F and G of a unitary space are defined.. as the ranges(or nullspacd of given rectangular matrices A and B. Accurate numerical methods are developed for computing the principal angles ek(F,G) and orthogonal sets of principal vectors u k 6 F and vk c G, k = 1,2,..., q = d ..."
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Cited by 164 (4 self)
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= dim(G) 2 dim(F). An important application in statistics is computing the canonical correlations uk = cos 8 k between two sets of variates. A perturbation analysis shows that the condition number for ek essentially is max(K(A),K(B)), where K denotes the condition number of a matrix. The algorithms
A Parallel Algorithm for Householder Tridiagonalization
 Proc. Fifth SIAM Conf. Appl. Linear Alg
, 1994
"... We present a parallel algorithm for reducing a dense symmetric matrix to tridiagonal form. The algorithm employs a square toruswrap mapping of matrix elements to processors to reduce communication and uses level 3 BLAS routines for efficient numerical kernels. We demonstrate the efficiency of this ..."
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Cited by 7 (0 self)
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We present a parallel algorithm for reducing a dense symmetric matrix to tridiagonal form. The algorithm employs a square toruswrap mapping of matrix elements to processors to reduce communication and uses level 3 BLAS routines for efficient numerical kernels. We demonstrate the efficiency
Fault tolerant QRDecomposition Algorithm Based on Householder
 Reflections and its Parallel Implementation, Proc.4th Int. Workshop Parallel Numerics`97
, 1997
"... Abstract. A faulttolerant algorithms based on Givens rotations and modified weighted checksum methods are proposed for matrix QRdecomposition. The purpose is to detect and correct the calculation errors occurred due to transient hardware faults during computation. The proposed algorithm enables to ..."
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Cited by 4 (1 self)
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Abstract. A faulttolerant algorithms based on Givens rotations and modified weighted checksum methods are proposed for matrix QRdecomposition. The purpose is to detect and correct the calculation errors occurred due to transient hardware faults during computation. The proposed algorithm enables
A fast randomized algorithm for the approximation of matrices
, 2007
"... We introduce a randomized procedure that, given an m×n matrix A and a positive integer k, approximates A with a matrix Z of rank k. The algorithm relies on applying a structured l × m random matrix R to each column of A, where l is an integer near to, but greater than, k. The structure of R allows u ..."
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Cited by 63 (7 self)
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that the spectral norm ‖A − Z ‖ of the discrepancy between A and Z is of the same order as √ max{m, n} times the (k + 1) st greatest singular value σk+1 of A, with small probability of large deviations. In contrast, the classical pivoted “Q R ” decomposition algorithms (such as GramSchmidt or Householder) require
Dynamic Bargaining In Households (with An Application To Bangladesh)
, 2002
"... . Much recent empirical work on intrahousehold allocation uses the axiomatic Nash Bargaining model to make predictions about how the distribution of consumption within the household will respond to individuals' income shocks. However, one of the basic axioms underlying this approach is tha ..."
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Cited by 34 (3 self)
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optimality with a weaker notion of eciency. We give a simple algorithm for computing allocations, and construct an extended example, meant to model the eects of Grameen Bank lending on intrahousehold allocation in Bangladesh. The model resolves a puzzle in the literature, namely, it predicts that women
An improved algorithm for computing the singular value decomposition
 ACM Trans. Math. Software
, 1982
"... The most wellknown and widely used algorithm for computing the Singular Value Decomposition (SVD) A U ~V T of an m x n rectangular matrix A is the GolubReinsch algorithm (GRSVD). In this paper, an improved version of the original GRSVD algorithm is presented. The new algorithm works best for ..."
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Cited by 57 (0 self)
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The most wellknown and widely used algorithm for computing the Singular Value Decomposition (SVD) A U ~V T of an m x n rectangular matrix A is the GolubReinsch algorithm (GRSVD). In this paper, an improved version of the original GRSVD algorithm is presented. The new algorithm works best
Block Reduction of Matrices to Condensed Forms for Eigenvalue Computations
 J. COMPUT. APPL. MATH
, 1987
"... In this paper we describe block algorithms for the reduction of a real symmetric matrix to tridiagonal form and for the reduction of a general real matrix to either bidiagonal or Hessenberg form using Householder transformations. The approach is to aggregate the transformations and to apply them in ..."
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Cited by 84 (15 self)
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In this paper we describe block algorithms for the reduction of a real symmetric matrix to tridiagonal form and for the reduction of a general real matrix to either bidiagonal or Hessenberg form using Householder transformations. The approach is to aggregate the transformations and to apply them
Accumulating householder transformations, revisited.
 ACM Trans. Math. Softw.,
, 2006
"... A theorem related to the accumulation of Householder transformations into a single orthogonal transformation known as the compact WY transform is presented. It provides a simple characterization of the computation of this transformation and suggests an alternative algorithm for computing it. It als ..."
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Cited by 6 (2 self)
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A theorem related to the accumulation of Householder transformations into a single orthogonal transformation known as the compact WY transform is presented. It provides a simple characterization of the computation of this transformation and suggests an alternative algorithm for computing it
Results 1  10
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442