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Volume 12, Issue 1
Weighted Integral of Infinitely Differentiable Multivariate Functions is Exponentially Convergent

Guiqiao Xu, Yongping Liu & Jie Zhang

Numer. Math. Theor. Meth. Appl., 12 (2019), pp. 98-114.

Published online: 2018-09

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  • Abstract

We study the problem of a weighted integral of infinitely differentiable multivariate functions defined on the unit cube with the $L$-norm of partial derivative of all orders bounded by 1. We consider the algorithms that use finitely many function values as information (called standard information). On the one hand, we obtained that the interpolatory quadratures based on the extended Chebyshev nodes of the second kind have almost the same quadrature weights. On the other hand, by using the  Smolyak algorithm with the above interpolatory quadratures, we proved that the weighted integral problem is of exponential convergence in the worst case setting.

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@Article{NMTMA-12-98, author = {Guiqiao Xu, Yongping Liu and Jie Zhang}, title = {Weighted Integral of Infinitely Differentiable Multivariate Functions is Exponentially Convergent}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2018}, volume = {12}, number = {1}, pages = {98--114}, abstract = {

We study the problem of a weighted integral of infinitely differentiable multivariate functions defined on the unit cube with the $L$-norm of partial derivative of all orders bounded by 1. We consider the algorithms that use finitely many function values as information (called standard information). On the one hand, we obtained that the interpolatory quadratures based on the extended Chebyshev nodes of the second kind have almost the same quadrature weights. On the other hand, by using the  Smolyak algorithm with the above interpolatory quadratures, we proved that the weighted integral problem is of exponential convergence in the worst case setting.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2017-0129}, url = {http://global-sci.org/intro/article_detail/nmtma/12692.html} }
TY - JOUR T1 - Weighted Integral of Infinitely Differentiable Multivariate Functions is Exponentially Convergent AU - Guiqiao Xu, Yongping Liu & Jie Zhang JO - Numerical Mathematics: Theory, Methods and Applications VL - 1 SP - 98 EP - 114 PY - 2018 DA - 2018/09 SN - 12 DO - http://doi.org/10.4208/nmtma.OA-2017-0129 UR - https://global-sci.org/intro/article_detail/nmtma/12692.html KW - AB -

We study the problem of a weighted integral of infinitely differentiable multivariate functions defined on the unit cube with the $L$-norm of partial derivative of all orders bounded by 1. We consider the algorithms that use finitely many function values as information (called standard information). On the one hand, we obtained that the interpolatory quadratures based on the extended Chebyshev nodes of the second kind have almost the same quadrature weights. On the other hand, by using the  Smolyak algorithm with the above interpolatory quadratures, we proved that the weighted integral problem is of exponential convergence in the worst case setting.

Guiqiao Xu, Yongping Liu and Jie Zhang. (2018). Weighted Integral of Infinitely Differentiable Multivariate Functions is Exponentially Convergent. Numerical Mathematics: Theory, Methods and Applications. 12 (1). 98-114. doi:10.4208/nmtma.OA-2017-0129
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