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Solving linear least squares problem with sparse-dense system matrix
 Karácsony, Eszter ; Tůma, Miroslav (advisor) ; Hnětynková, Iveta (referee)
This thesis deals with the method of least squares with matrices that are partly dense and partly sparse. The first part of the thesis describes the method of least squares and its basic properties. Then theoretical foundations of how to proceed with the solution of a system of equations with a sparse-dense matrix using the method of least squares are described. In particular, the approach of the thesis leads to the derivation of the CGLS algorithm with preconditioning. In the experiments, the preconditioned CGLS method is applied to test matrices that are partly dense and partly sparse. For example, changes in computational time and related changes in iteration counts depending on the number of additional dense rows in the matrices are investigated. The results are illustrated by several plots. 1
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