Mastronardi, Lemmerling, and van Huffel presented an algorithm for solving a total least squares problem when the matrix and its perturbations are Toeplitz. A Toeplitz matrix is a special kind of matrix with small displacement rank. Here we generalize the fast algorithm to any matrix with small displacement rank. In particular, we show how to efficiently construct the generators whenever M has small displacement rank and show that in many important cases the Cholesky factorization of the matrix M^TM can also be determined fast. We further extend this problem to Tikhonov regularization of ill-posed problems and illustrate the use of the algorithm on an image deblurring problem. (Also UMIACS-TR-2002-70
A Newton method to solve total least squares problems for Toeplitz systems of equations is considere...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
In this contribution we extend the result of (Markovsky et. al, SIAM J. of Matrix Anal. and Appl., 2...
In this thesis, we present the O(n(log n)^2) superfast linear least squares Schur algorithm (ssschur...
Rosen, Park and Glick proposed the structured total least norm (STLN) algorithm for solving problem...
AbstractThe total least squares (TLS) method is a successful approach for linear problems when not o...
Least squares estimations have been used extensively in many applications system identification and ...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-condition...
AbstractThe structured total least squares (STLS) problem has been introduced to handle problems inv...
Many problems in science and engineering give rise to linear systems of equations that are commonly ...
AbstractWe describe a systolic algorithm for solving a Toeplitz least-squares problem of special for...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
The Total Least Squares solution of an overdetermined, approximate linear equation Ax approx b minim...
AbstractIn this paper we develop a superfast O((m+n)log2(m+n)) complexity algorithm to solve a linea...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
A Newton method to solve total least squares problems for Toeplitz systems of equations is considere...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
In this contribution we extend the result of (Markovsky et. al, SIAM J. of Matrix Anal. and Appl., 2...
In this thesis, we present the O(n(log n)^2) superfast linear least squares Schur algorithm (ssschur...
Rosen, Park and Glick proposed the structured total least norm (STLN) algorithm for solving problem...
AbstractThe total least squares (TLS) method is a successful approach for linear problems when not o...
Least squares estimations have been used extensively in many applications system identification and ...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-condition...
AbstractThe structured total least squares (STLS) problem has been introduced to handle problems inv...
Many problems in science and engineering give rise to linear systems of equations that are commonly ...
AbstractWe describe a systolic algorithm for solving a Toeplitz least-squares problem of special for...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
The Total Least Squares solution of an overdetermined, approximate linear equation Ax approx b minim...
AbstractIn this paper we develop a superfast O((m+n)log2(m+n)) complexity algorithm to solve a linea...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
A Newton method to solve total least squares problems for Toeplitz systems of equations is considere...
AbstractThis paper discusses the solution of large-scale linear discrete ill-posed problems with a n...
In this contribution we extend the result of (Markovsky et. al, SIAM J. of Matrix Anal. and Appl., 2...