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Solvability classes for core problems in matrix total least squares minimization

Iveta HnětynkováMartin PlešingerJana Žáková — 2019

Applications of Mathematics

Linear matrix approximation problems A X B are often solved by the total least squares minimization (TLS). Unfortunately, the TLS solution may not exist in general. The so-called core problem theory brought an insight into this effect. Moreover, it simplified the solvability analysis if B is of column rank one by extracting a core problem having always a unique TLS solution. However, if the rank of B is larger, the core problem may stay unsolvable in the TLS sense, as shown for the first time by Hnětynková,...

Filter factors of truncated TLS regularization with multiple observations

Iveta HnětynkováMartin PlešingerJana Žáková — 2017

Applications of Mathematics

The total least squares (TLS) and truncated TLS (T-TLS) methods are widely known linear data fitting approaches, often used also in the context of very ill-conditioned, rank-deficient, or ill-posed problems. Regularization properties of T-TLS applied to linear approximation problems A x b were analyzed by Fierro, Golub, Hansen, and O’Leary (1997) through the so-called filter factors allowing to represent the solution in terms of a filtered pseudoinverse of A applied to b . This paper focuses on the situation...

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