Adjoint-based algorithms and numerical methods for sensitivity generation and optimization of large scale dynamic systems

By J. Albersmeyer. 2010

54 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/496ea348008a4f99e33cfabca6a5339de263449a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Moritz Diehl
2. Rien Quirynen
3. Joel A. E. Andersson
4. Gianluca Frison
5. Robin Verschueren

In Computational Theory and Mathematics

1. Rien Quirynen
2. Moritz Diehl
3. Joel A. E. Andersson
4. Gianluca Frison
5. Robin Verschueren

In Numerical Analysis

1. Gianluca Frison
2. Moritz Diehl
3. Rien Quirynen
4. Joel A. E. Andersson
5. Robin Verschueren

Citations in the corpus

• acados—a modular open-source framework for fast embedded optimal control (2019, methodology): “[29]. In case of an implicit integration scheme, the IND approach either results in iterative dierentiation techniques or a direct computation of sensitivities based on the implicit function theorem [2]. In addition, forward-backward propagation schemes can be derived to compute the symmetric Hessia”
• A General-Purpose Software Framework for Dynamic Optimization (Een algemene softwareomgeving voor dynamische optimalisatie) (2013, methodology): “AD Algorithmic differentiation BDF Backward differentiation formulas CCS Compressed column storage CPU Central processing unit CRS Compressed row storage DAE Differential-algebraic equation END External numerical differentiation GN Gauss-Newton GPU Graphics processing unit IND Internal numerical dif”