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7,469
Least-squares Problems
"... The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, ..."
Abstract
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Cited by 1 (1 self)
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The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates
nonnegative least squares problems
, 2004
"... interior-point gradient method for large-scale totally ..."
Rank Degeneracy and Least Squares Problems
, 1976
"... This paper is concerned with least squares problems when the least squares matrix A is near a matrix that is not of full rank. A definition of numerical rank is given. It is shown that under certain conditions when A has numerical rank r there is a distinguished r dimensional subspace of the column ..."
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Cited by 56 (2 self)
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This paper is concerned with least squares problems when the least squares matrix A is near a matrix that is not of full rank. A definition of numerical rank is given. It is shown that under certain conditions when A has numerical rank r there is a distinguished r dimensional subspace of the column
Robust Solutions To Least-Squares Problems With Uncertain Data
, 1997
"... . We consider least-squares problems where the coefficient matrices A; b are unknown-butbounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpret ..."
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Cited by 205 (14 self)
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. We consider least-squares problems where the coefficient matrices A; b are unknown-butbounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can
On perturbations of linear least squares problems
"... The asymptotic behaviour of a class of least squares problems when subjected to structured perturbations is considered. It is permitted that the number of rows (observations) in the design matrix can be unbounded while the number of degrees of freedom (variables) is fixed. It is shown that for certa ..."
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The asymptotic behaviour of a class of least squares problems when subjected to structured perturbations is considered. It is permitted that the number of rows (observations) in the design matrix can be unbounded while the number of degrees of freedom (variables) is fixed. It is shown
Topics in Sparse Least Squares Problems
- Linkoping University, Linkoping, Sweden, Dept. of Mathematics
, 2000
"... This thesis addresses topics in sparse least squares computation. A stable method for solving the least squares problem, min kAx; bk2 is based on the QR factorization. Here we haveaddressed the di culty for storing the orthogonal matrix Q. Using traditional methods, the number of nonzero elements in ..."
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Cited by 10 (0 self)
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This thesis addresses topics in sparse least squares computation. A stable method for solving the least squares problem, min kAx; bk2 is based on the QR factorization. Here we haveaddressed the di culty for storing the orthogonal matrix Q. Using traditional methods, the number of nonzero elements
The Numerical Solution of Constrained Linear Least-squares Problems
, 1981
"... The paper describes a numerically stable algorithm to solve constrained linear least-squares problems and allows rank deficient or underdetermined observation matrices. The method starts with the calculation of the rank of the observation matrix and the transformation into a least distance problem. ..."
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The paper describes a numerically stable algorithm to solve constrained linear least-squares problems and allows rank deficient or underdetermined observation matrices. The method starts with the calculation of the rank of the observation matrix and the transformation into a least distance problem
Results 1 - 10
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7,469