Results 1  10
of
978,695
Leastsquares Problems
"... The multilinear leastsquares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrixvector product. The MLLS is typically a largescale problem characterized by a large number of local minimizers. It originates, ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
The multilinear leastsquares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrixvector product. The MLLS is typically a largescale problem characterized by a large number of local minimizers. It originates
nonnegative least squares problems
, 2004
"... interiorpoint gradient method for largescale 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 ..."
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 LeastSquares Problems With Uncertain Data
, 1997
"... . We consider leastsquares problems where the coefficient matrices A; b are unknownbutbounded. We minimize the worstcase residual error using (convex) secondorder cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpret ..."
Abstract

Cited by 203 (14 self)
 Add to MetaCart
. We consider leastsquares problems where the coefficient matrices A; b are unknownbutbounded. We minimize the worstcase residual error using (convex) secondorder 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 ..."
Abstract
 Add to MetaCart
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 ..."
Abstract

Cited by 9 (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 Leastsquares Problems
, 1981
"... The paper describes a numerically stable algorithm to solve constrained linear leastsquares 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. ..."
Abstract
 Add to MetaCart
The paper describes a numerically stable algorithm to solve constrained linear leastsquares 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
of
978,695