Global optimization for low-dimensional switching linear regression and bounded-error estimation

18 Jul 2017Fabien Lauer

The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only under restrictive conditions, the proposed approach always yields a solution with a certificate of global optimality... (read more)

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