Multiobjective optimization via filtering methods
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In many applications it is required to determine a parameters set which is suitable for competing models. This leads to solving multi-objective optimization problems. In this talk, we investigate the use of ensemble methods, in particular the ensemble Kalman Filter method, to solve coupled inverse nonlinear problems through a weighted function approach. Moreover, an explicit update formula for the weights analyzing the mean field limit of the ensemble method will be presented and numerical tests will show the improved performance of the proposed method.
