Filter factors of truncated tls regularization with multiple observations

dc.contributor.authorPlešinger Martincs
dc.contributor.authorŽáková Janacs
dc.contributor.authorHnětynková Ivetacs
dc.date.accessioned2018-09-25T12:15:50Z
dc.date.available2018-09-25T12:15:50Z
dc.date.issued2017cs
dc.description.abstractThe 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 $Ax\approx b$ 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 when multiple observations $b_1,\ldots,b_d$ are available, i.e., the T-TLS method is applied to the problem $AX\approx B$, where $B=[b_1,\ldots,b_d]$ is a matrix. It is proved that the filtering representation of the T-TLS solution can be generalized to this case. The corresponding filter factors are explicitly derived.
dc.format.extent16cs
dc.identifier.doi10.21136/AM.2017.0228-16
dc.identifier.issn0862-7940cs
dc.identifier.urihttps://dspace.tul.cz/handle/15240/31445
dc.identifier.urihttps://articles.math.cas.cz/10.21136/AM.2017.0228-16
dc.language.isoengcs
dc.publisherSpringercs
dc.relation.ispartofseries0cs
dc.relation.urihttps://link.springer.com/article/10.21136/AM.2017.0228-16cs
dc.subjecttruncated total least squarescs
dc.subjectmultiple right-hand sidescs
dc.subjecteigenvalues of rank-d updatecs
dc.subjectill-posed problemcs
dc.subjectregularizationcs
dc.subjectfilter factorscs
dc.titleFilter factors of truncated tls regularization with multiple observationsen
local.citation.epage105-120cs
local.citation.spage105-120cs
local.identifier.publikace4908
local.identifier.wok400889400002en
local.relation.issue2cs
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