RT Journal Article
T1 Parameter estimation in ODEs: assessing the potential of local and global solvers
A1 Fernández de Dios, M.
A1 González Rueda, Ángel Manuel
A1 Rodríguez Banga, Julio
A1 González Díaz, Julio
A1 Rodríguez Penas, David
K1 Parameter estimation
K1 Dynamic modelling
K1 Optimization
K1 Mathematical programming
AB We consider the problem of parameter estimation in dynamic systems described by ordinary differential equations. A review of the existing literature emphasizes the need for deterministic global optimization methods due to the nonconvex nature of these problems. Recent works have focused on expanding the capabilities of specialized deterministic global optimization algorithms to handle more complex problems. Despite advancements, current deterministic methods are limited to problems with a maximum of around five state and five decision variables, prompting ongoing efforts to enhance their applicability to practical problems. Our study seeks to assess the effectiveness of state-of-the-art general-purpose global and local solvers in handling realistic-sized problems efficiently, and evaluating their capabilities to cope with the nonconvex nature of the underlying estimation problems.
PB Springer
SN 1389-4420
YR 2025
FD 2025-06-05
LK https://hdl.handle.net/10347/42438
UL https://hdl.handle.net/10347/42438
LA eng
NO de Dios, M.F., González-Rueda, Á.M., Banga, J.R. et al. Parameter estimation in ODEs: assessing the potential of local and global solvers. Optim Eng (2025). https://doi.org/10.1007/s11081-025-09978-9
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the R&D projects PID2021-124030NB-C31 and PID2021-124030NB-C32 funded by MICIU/AEI/10.13039/501100011033/ and by ERDF/EU. This research was also funded by Grupos de Referencia Competitiva ED431C-2021/24 from the Consellería de Cultura, Educación e Universidades, Xunta de Galicia. JRB acknowledges support from grant PID2020-117271RB-C22 (BIODYNAMICS) funded by MCIN/AEI/10.13039/501100011033, from grant PID2023-146275NB-C22 (DYNAMO-bio) funded by MICIU/AEI/ 10.13039/501100011033 and ERDF/EU, and from grant CSIC PIE 202470E108 (LARGO). The authors acknowledge CESGA (Centro de Supercomputación de Galicia) for providing access to its FinisTerrae III supercomputer.
DS Minerva
RD 26 abr 2026