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1,731,056
SPARSE MATRIX METHODS IN OPTIMIZATION
, 1984
"... Optimization algorithms typically require the solution of many systems of linear equations Bkyk b,. When large numbers of variables or constraints are present, these linear systems could account for much of the total computation time. Both direct and iterative equation solvers are needed in practi ..."
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Cited by 18 (4 self)
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review various sparse matrices that arise in optimization, and discuss compromises that are currently being made in dealing with them. Since significant advances continue to be made with singlesystem solvers, we give special attention to methods that allow such solvers to be used repeatedly on a
Sparse Matrix Methods for Wavefront Reconstruction Revisited
"... This paper contains a review of sparse matrix methods for openloop wavefront estimation in astronomical adaptive optics systems with a large number of degrees of freedom. We address shortcomings of existing sparse methods for multiconjugate adaptive optics and propose some alternative approaches. W ..."
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This paper contains a review of sparse matrix methods for openloop wavefront estimation in astronomical adaptive optics systems with a large number of degrees of freedom. We address shortcomings of existing sparse methods for multiconjugate adaptive optics and propose some alternative approaches
SPARSE MATRIX METHOD FOR COMPONENTORIENTED DYNAMIC SIMULATION OF MULTIBODIES IN VSD SOFTWARE
, 2007
"... ..."
HighOrder Multivariable Transfer Function Curve Fitting: Algorithms, Sparse Matrix Methods and Experimental Results
 Automatica
, 1994
"... This paper develops a computational approach to frequency domain curve fitting, based on 2norm minimization, The algorithm is specifically tailored to the identification of complex systems having a large number of parameters, and a sparse matrix method for reducing computation and memory requiremen ..."
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Cited by 9 (0 self)
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This paper develops a computational approach to frequency domain curve fitting, based on 2norm minimization, The algorithm is specifically tailored to the identification of complex systems having a large number of parameters, and a sparse matrix method for reducing computation and memory
TimeMemory TradeOffs Using Sparse Matrix Methods for LargeScale Eigenvalue Problems ⋆
"... Abstract. Iterative methods such as Lanczos and JacobiDavidson are typically used to compute a small number of eigenvalues and eigenvectors of a sparse matrix. However, these methods are not effective in certain largescale applications, for example, “global tight binding molecular dynamics.” Such ..."
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Cited by 1 (0 self)
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Abstract. Iterative methods such as Lanczos and JacobiDavidson are typically used to compute a small number of eigenvalues and eigenvectors of a sparse matrix. However, these methods are not effective in certain largescale applications, for example, “global tight binding molecular dynamics
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
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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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
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure
Nonnegative matrix factorization with sparseness constraints
 Jour. of
, 2004
"... www.cs.helsinki.fi/patrik.hoyer ..."
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
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Cited by 958 (5 self)
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This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
Results 1  10
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