A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Abstract
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.
Cite
Text
Raziperchikolaei and Bhat. "A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation." International Conference on Machine Learning, 2019.Markdown
[Raziperchikolaei and Bhat. "A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/raziperchikolaei2019icml-block/)BibTeX
@inproceedings{raziperchikolaei2019icml-block,
title = {{A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation}},
author = {Raziperchikolaei, Ramin and Bhat, Harish},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {5380-5388},
volume = {97},
url = {https://mlanthology.org/icml/2019/raziperchikolaei2019icml-block/}
}