@inproceedings{9a97ed843efe477a9f29cf0dcfdaf17e,
title = "Parallelisable computation of the gradient in nonlinear stochastic optimal control problems",
abstract = "Nonlinear (deterministic and stochastic) optimal control problems are often solved on embedded devices using first-order numerical optimisation methods. The gradient computation accounts for a significant part of the computation cost per iteration; this is often performed with reverse-mode automatic differentiation and software libraries such as CasADi can be used to generate C code for this computation. In this paper, we propose a simple ad hoc and highly parallelisable algorithm for the computation of the gradient of the total cost for deterministic and stochastic scenario-based optimal control problems. We also present gradgen: an open-source Python package that generates Rust code for the gradient computation. The proposed method leads to a faster performance compared to CasADi and a significant reduction in generated code.",
keywords = "math.OC",
author = "Jie Lin and Ruairi Moran and Pantelis Sopasakis",
year = "2023",
month = jul,
day = "3",
doi = "10.1109/ISSC59246.2023.10162071",
language = "English",
isbn = "9798350340587",
series = "Irish Signals and Systems Conference (ISSC): Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 34th Irish Signals and Systems Conference (ISSC): Proceedings",
address = "United States",
}