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Least-squares Problems

by Hans Knutsson A, Spartak Zikrin B, Mats Andersson, Oleg Burdakov, Hans Knutsson, Spartak Zikrin
"... 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 - Cited by 1 (1 self) - Add to MetaCart
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

least-squares problems

by C. Cartis, N. I. M. Gould, P. L. Toint, C. Cartis, N. I. M. Gould, P. L. Toint, C. Cartis Et Al
"... Trust-region and other regularisations of linear ..."
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Trust-region and other regularisations of linear

nonnegative least squares problems

by Michael Merritt, Yin Zhang , 2004
"... interior-point gradient method for large-scale totally ..."
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interior-point gradient method for large-scale totally

Least Squares Problems in Orthornormalization

by Shanwen Hu
"... ar ..."
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Abstract not found

Rank Degeneracy and Least Squares Problems

by Gene Golub, Virginia Klema, G. W. Stewart , 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 ..."
Abstract - Cited by 56 (2 self) - Add to MetaCart
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

by Laurent El Ghaoui, Hervé Lebret , 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 ..."
Abstract - Cited by 205 (14 self) - Add to MetaCart
. 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

Solution of Nonlinear Least-Squares Problems

by Christina Fraley, S Electe, Christina Fraley, Christina Fraley , 1987
"... im, F rnp& ..."
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im, F rnp&

On perturbations of linear least squares problems

by M.R. Osborne
"... 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

by Mikael Adlers - 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 ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
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

by Klaus Schrmcowski , 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
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