Abstract
This contribution is a sequel of the report (Bompard et al. in http://hal.inria.fr/inria-00526558/en/, 2010). In PDE-constrained global optimization (e.g., Jones (in J. Global Optim. 21(4):345–383, 2001)), iterative algorithms are commonly efficiently accelerated by techniques relying on approximate evaluations of the functional to be minimized by an economical but lower-fidelity model (“meta-model”), in a so-called “Design of Experiment” (DoE) (Sacks et al. in Stat. Sci. 4(4):409–435, 1989). Various types of meta-models exist (interpolation polynomials, neural networks, Kriging models, etc.). Such meta-models are constructed by pre-calculation of a database of functional values by the costly high-fidelity model. In adjoint-based numerical methods, derivatives of the functional are also available at the same cost, although usually with poorer accuracy. Thus, a question arises: should the derivative information, available but known to be less accurate, be used to construct the meta-model or be ignored? As the first step to investigate this issue, we consider the case of the Hermitian interpolation of a function of a single variable, when the function values are known exactly, and the derivatives only approximately, assuming a uniform upper bound ϵ on this approximation is known. The classical notion of best approximation is revisited in this context, and a criterion is introduced to define the best set of interpolation points. This set is identified by either analytical or numerical means. If n+1 is the number of interpolation points, it is advantageous to account for the derivative information when ϵ≤ϵ 0, where ϵ 0 decreases with n, and this is in favor of piecewise, low-degree Hermitian interpolants. In all our numerical tests, we have found that the distribution of Chebyshev points is always close to optimal, and provides bounded approximants with close-to-least sensitivity to the uncertainties.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bompard M, Désidéri J-A, Peter J (2010) Best Hermitian interpolation in presence of uncertainties. INRIA Research Report RR-7422, Oct. http://hal.inria.fr/inria-00526558/en/
Conte SD, de Boor C (1972) Elementary numerical analysis: an algorithmic approach, 2nd edn. McGraw-Hill, New York
Jones D (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21(4):345–383
Pearson CE (ed) (1990) Handbook of applied mathematics: selected results and methods, 2nd edn. Van Nostrand Reinhold, New York
Quarteroni A, Sacco R, Saleri F (2007) Numerical mathematics, 2nd edn. Texts in applied mathematics, vol 37. Springer, Berlin
Sacks J, Welch W, Mitchell T, Wynn H (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–435
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Désidéri, JA., Bompard, M., Peter, J. (2013). Hermitian Interpolation Subject to Uncertainties. In: Repin, S., Tiihonen, T., Tuovinen, T. (eds) Numerical Methods for Differential Equations, Optimization, and Technological Problems. Computational Methods in Applied Sciences, vol 27. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5288-7_11
Download citation
DOI: https://doi.org/10.1007/978-94-007-5288-7_11
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5287-0
Online ISBN: 978-94-007-5288-7
eBook Packages: EngineeringEngineering (R0)