Journal article
Solving the Dym initial value problem in reproducing kernel space
- Abstract:
- We consider two numerical solution approaches for the Dym initial value problem using the reproducing kernel Hilbert space method. For each solution approach, the solution is represented in the form of a series contained in the reproducing kernel space, and a truncated approximate solution is obtained. This approximation converges to the exact solution of the Dym problem when a sufficient number of terms are included. In the first approach, we avoid to perform the Gram-Schmidt orthogonalization process on the basis functions, and this will decrease the computational time. Meanwhile, in the second approach, working with orthonormal basis elements gives some numerical advantages, despite the increased computational time. The latter approach also permits a more straightforward convergence analysis. Therefore, there are benefits to both approaches. After developing the reproducing kernel Hilbert space method for the numerical solution of the Dym equation, we present several numerical experiments in order to show that the method is efficient and can provide accurate approximations to the Dym initial value problem for sufficiently regular initial data after relatively few iterations. We present the absolute error of the results when exact solutions are known and residual errors for other cases. The results suggest that numerically solving the Dym initial value problem in reproducing kernel space is a useful approach for obtaining accurate solutions in an efficient manner.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Accepted manuscript, pdf, 108.7KB, Terms of use)
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- Publisher copy:
- 10.1007/s11075-017-0381-2
Authors
- Publisher:
- Springer
- Journal:
- Numerical Algorithms More from this journal
- Volume:
- 78
- Issue:
- 2
- Pages:
- 405–421
- Publication date:
- 2017-07-22
- Acceptance date:
- 2017-07-10
- DOI:
- EISSN:
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1572-9265
- ISSN:
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1017-1398
- Keywords:
- Pubs id:
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pubs:710845
- UUID:
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uuid:1e902e82-c09b-4344-a9e6-3234e0b92030
- Local pid:
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pubs:710845
- Source identifiers:
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710845
- Deposit date:
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2017-08-25
- ARK identifier:
Terms of use
- Copyright holder:
- Springer Science+Business Media
- Copyright date:
- 2017
- Notes:
- © Springer Science+Business Media, LLC 2017. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/s11075-017-0381-2
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