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Adjoint-based optimization of experimental designs with many control variables

  • We propose a method for an efficient optimization of experimental designs, using a combination of discrete adjoint computations, Taylor arithmetic and matrix calculus. Compared to the state of the art of using finite differences or the forward mode of automatic differentiation, our proposed approach leads to a reduction of the relative temporal complexity from linear to constant time in the number of control variables and measurement weights. We demonstrate that the advantageous complexity results are not only of theoretical nature, but lead to significant speedups in practice as well. With our implementation we are very close to the theoretical bound of the cheap gradient principle. We present one academic (spatially discretized heat equation) and two industrial application examples (biochemical process/Diesel-oxidation catalysis process) where we achieve speedups that range between 10 and 100. In addition to our core results, we also describe an efficient adjoint approach for the treatment of differential algebraic equations and present adjoint formulas for constrained least-squares problems.

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Metadaten
Author:Sebastian F. Walter, Andrea Schmidt, Stefan KörkelGND
DOI:https://doi.org/10.1016/j.jprocont.2014.06.019
ISSN:0959-1524
Parent Title (German):Journal of Process Control
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:German
Year of first Publication:2014
Release Date:2022/12/08
Tag:Automatic differentiation; Constrained least-squares; Differential algebraic equation; Optimum experimental design; Reverse mode
Volume:24
Issue:10
First Page:1504
Last Page:1515
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG