## AbstractRecent advances in total least squares approaches for solving various errors-in-variables modeling problems are reviewed, with emphasis on the following generalizations: 1. the use of weighted norms as a measure of the data perturbation size, capturing prior knowledge about uncertainty in the data; 2. the addition of constraints on the perturbation to preserve the structure of the data matrix, motivated by structured data matrices occurring in signal and image processing, systems and control, and computer algebra; 3. the use of regularization in the problem formulation, aiming at stabilizing the solution by decreasing the effect due to intrinsic ill-conditioning of certain problems.
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