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Long steps in IPM and L_1-regression
 Šicková, Barbora ; Černý, Michal (advisor) ; Sokol, Ondřej (referee)
This work deals with Newton method of interior point which is applied on finding estimation of polynomial L_1 regression. The aim of this thesis is to find new modifications of long step choice in Newton method in order to find faster solution of L_1 estimations for large data sets. Designed modifications are based on full-Newton step algorithm for the self-dual model. According to reasults, the best are algorithms AF-L, F-LP1, AF-LP1 a AF-L-mixed, which modify the barrier update parameter in adaptive way. Algorithms and obtained results were implemented end visualized in MatLab.
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