Algebraic subfamily optimization

  • Emil Horobet (Sapientia Hungarian University of Transylvania, Târgu-Mureş)
E1 05 (Leibniz-Saal)


Algebraic methods for optimization have been used for maximum likelihood estimation and optimizing Euclidean distance. A complexity measure of these problems is given by the algebraic degree of the problem. This is a good measure because it counts the number of trials needed to solve the problem. These optimization problems are described by a correspondence between critical points, Lagrange-multipliers, and functions to be optimized. In this talk we present a study of subfamilies of these problems. Our main contribution is to formulate a unified duality theory encompassing maximum likelihood degree and Euclidean distance degree duality. We use this duality theory to develop algorithms and describe special loci. Moreover, we do a case analysis for problems in statistics, kinematics, and engineering.

Mirke Olschewski

MPI for Mathematics in the Sciences Contact via Mail

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