A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
This work addresses a computational bottleneck in ODE modeling for researchers and practitioners, offering a more efficient and reliable method, though it appears incremental as an algorithmic improvement.
The authors tackled the problem of simultaneous filtering and parameter estimation for ODE models by proposing a block coordinate descent proximal algorithm (BCD-prox), which demonstrated increased robustness, decreased training times, and improved accuracy in systems with up to 40 dimensions compared to state-of-the-art methods.
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.