A stable and efficient algorithm for nonlinear orthogonal distance regression
SIAM Journal on Scientific and Statistical Computing
Total Least Norm Formulation and Solution for Structured Problems
SIAM Journal on Matrix Analysis and Applications
Robust solution to least squares problems with uncertain data
Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling
Structured total least squares problems: formulations, algorithms and applications
Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
An Efficient Algorithm for a Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications
An Efficient Primal-Dual Interior-Point Method for Minimizing a Sum of Euclidean Norms
SIAM Journal on Scientific Computing
Data Fitting Problems with Bounded Uncertainties in the Data
SIAM Journal on Matrix Analysis and Applications
Robust Solutions to a General Class of Approximation Problems
SIAM Journal on Scientific Computing
A Global Solution for the Structured Total Least Squares Problem with Block Circulant Matrices
SIAM Journal on Matrix Analysis and Applications
Robust solutions of uncertain linear programs
Operations Research Letters
Editorial: Total Least Squares and Errors-in-variables Modeling
Computational Statistics & Data Analysis
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Linear data fitting problems with uncertain data which lie in a given uncertainty set are considered. A robust counterpart of such a problem may be interpreted as the problem of finding a solution which is best over all possible perturbations of the data which lie in the set. In particular, robust counterparts of total least squares problems have been studied and good algorithms are available. Robust counterparts of the problems considered as errors-in-variables problems are considered, when it is appropriate to work directly with the uncertain variable values. It is shown how the original problems can be replaced by convex optimization problems in fewer variables for which standard software may be applied.