We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models. As we show on ODE systems with up to d=40 dimensions, as compared to state-of-the-art methods, BCD-prox exhibits increased robustness (to noise, parameter initialization, and hyperparameters), decreased training times, and improved accuracy of both filtered states and estimated parameters. We show how BCD-prox can be used with multistep numerical discretizations, and we establish convergence of BCD-prox under hypotheses that include real systems of interest.
Ramin Raziperchikolaei (UC MErced)
Harish Bhat (University of California, Merced)
Related Events (a corresponding poster, oral, or spotlight)
2019 Oral: A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation »
Tue Jun 11th 02:00 -- 02:20 PM Room Room 201