National Repository of Grey Literature 4 records found  Search took 0.00 seconds. 
Properties and construction of core problem in data fitting problems with multiple observations
Dvořák, Jan ; Hnětynková, Iveta (advisor) ; Plešinger, Martin (referee)
In this work we study the solution of linear approximation problems with multiple observations. Particulary we focus on the total least squares method, which belogs to the class of ortogonaly invariant problems. For these problems we describe the so called core reduction. The aim is to reduce dimesions of the problem while preserving the solution, if it exists. We present two ways of constructing core problems. One is based on the singular value decomposition and the other uses the generalized Golub-Kahan iterative bidiago- nalization. Further we investigate properties of the core problem and of the methods for its construction. Finally we preform numerical experiments in the Matlab enviroment in order to test the reliability of the discussed algorithms. 1
Lanczošova třídiagonalizace, Golub-Kahanova bidiagonalizace a core problém
Hnětynková, Iveta ; Strakoš, Zdeněk
Consider an orthogonally invariant linear approximation problem Ax ~ b. In "C.C. Paige, Z. Strakoš: Core problems in linear algebraic systems (SIAM J. Matrix Anal. Appl. 27 (2006), pp. 861-875)" it was proved that the partial upper bidiagonalization of the matrix [b,A] determines a core approximation problem that contains the necessary and sufficient information for solving the original problem. Our contribution derives the fundamental characteristics of the core problem from the known relationship between the Golub-Kahan bidiagonalization, the Lanczos tridiagonalization and the properties of Jacobi matrices.

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