Abstract
We provide a general discussion of Smolyak’s algorithm for the acceleration of scientific computations. The algorithm first appeared in Smolyak’s work on multidimensional integration and interpolation. Since then, it has been generalized in multiple directions and has been associated with the keywords: sparse grids, hyperbolic cross approximation, combination technique, and multilevel methods. Variants of Smolyak’s algorithm have been employed in the computation of high-dimensional integrals in finance, chemistry, and physics, in the numerical solution of partial and stochastic differential equations, and in uncertainty quantification. Motivated by this broad and ever-increasing range of applications, we describe a general framework that summarizes fundamental results and assumptions in a concise application-independent manner.
Original language | English (US) |
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Title of host publication | Lecture Notes in Computational Science and Engineering |
Publisher | Springer Verlag |
Pages | 201-228 |
Number of pages | 28 |
DOIs | |
State | Published - 2018 |
Publication series
Name | Lecture Notes in Computational Science and Engineering |
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Volume | 123 |
ISSN (Print) | 1439-7358 |
Bibliographical note
Publisher Copyright:© 2018, Springer International Publishing AG, part of Springer Nature.
ASJC Scopus subject areas
- Modeling and Simulation
- General Engineering
- Discrete Mathematics and Combinatorics
- Control and Optimization
- Computational Mathematics