Results 221 to 230 of about 150,932 (264)
Matrix Algebras and Displacement Decompositions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2000 This paper investigates classes of complex \(n\times n\) matrices for which there are formulae enabling computation of a matrix vector product \(Af\) by means of a small number of fast discrete transforms. The basic formula is the ``displacement formula'': \(A=\sum_{m=1}^{\alpha}L_{m}U_{m}\) where \(L_{m}\) and \(U_{m}\) are lower and upper triangular ...
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On LU decomposition of a centrosymmetric matrix
Information Sciences, 1992An \(LU\) decomposition of a centrosymmetric matrix, the Choleski decomposition of a centrosymmetric, symmetric and positively defined matrix, as well as algorithms for finding the inverse of such a matrix by using this decomposition are presented in the paper.
Ivatury Ramabhadrasarma +2 more
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On ANOVA-Like Matrix Decompositions [PDF]
, 2015 The analysis of variance plays a fundamental role in statistical theory and practice, the standard Euclidean geometric form being particularly well established. The geometry and associated linear algebra underlying such standard analysis of variance methods permit, essentially direct, generalisation to other settings. Specifically, as jointly developed
BOVE, Giuseppe +3 more
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N-decomposition and decomposition matrix for automata
Proceedings of the annual conference on - ACM'73, 1973This continues the study on generalized mutiple decomposition allowing 2-way interconnection [1]. Let NeZ+.An automaton M e D, T,F> is an N-automaton iff the set of states D ≤ πSi and each Si e πi (D) where πi is the projection map onto the ith component.
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Accelerating matrix decomposition with replications
2008 IEEE International Symposium on Parallel and Distributed Processing, 2008Matrix decomposition applications that involve large matrix operations can take advantage of the flexibility and adaptability of reconfigurable computing systems to improve performance. The benefits come from replication, which includes vertical replication and horizontal replication.
Yi-Gang Tai +2 more
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Matrix decomposition on the star graph
IEEE Transactions on Parallel and Distributed Systems, 1997We present and evaluate, for the first time, a parallel algorithm for solving the LU decomposition problem on the star graph. The proposed parallel algorithm is of O(N/sup 3//n!) computation complexity and uses O(Nn) communication time to decompose a matrix of order N on a star graph of dimension n, where N/spl ges/(n-1)!.
Abdel Elah Al-Ayyoub, Khaled Day
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On Huynen's Decomposition of a Kennaugh Matrix
IEEE Geoscience and Remote Sensing Letters, 2006For some special case, Huynen's decomposition cannot be used to extract a desired target from an average Kennaugh matrix. In this paper, the authors modify Huynen's method for overcoming its disadvantage, based on a simple transform of a Kennaugh matrix.
Jian Yang 0011 +3 more
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Interpretable nonnegative matrix decompositions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008A matrix decomposition expresses a matrix as a product of at least two factor matrices. Equivalently, it expresses each column of the input matrix as a linear combination of the columns in the first factor matrix. The interpretability of the decompositions is a key issue in many data-analysis tasks. We propose two new matrix-decomposition problems: the
Saara Hyvönen +2 more
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A QR Decomposition for Matrix Pencils
BIT Numerical Mathematics, 2000An efficient and numerically stable modification of the \(QR\) decomposition for solving a linear least squares problem with a matrix of the form \(A+\lambda B\) is given. The idea is to proceed by columns and in step \(i\) the algorithm is driven by data from column \(i\) of the transformed matrices \(B\) and \(A\) in turn.
Spellucci, P., Hartmann, W. M.
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Extended Boolean Matrix Decomposition
2009 Ninth IEEE International Conference on Data Mining, 2009With the vast increase in collection and storage of data, the problem of data summarization is most critical for effective data management. Since much of this data is categorical in nature, it can be viewed in terms of a Boolean matrix. Boolean matrix decomposition (BMD) has been used to provide concise and interpretable representations of Boolean data
Haibing Lu +3 more
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