%0 Generic %1 gilbert2017efficient %A Gilbert, Alexander D. %A Kuo, Frances Y. %A Nuyens, Dirk %A Wasilkowski, Grzegorz W. %D 2017 %K approximation quadrature %T Efficient implementations of the Multivariate Decomposition Method for approximating infinite-variate integrals %U http://arxiv.org/abs/1712.06782 %X In this paper we focus on efficient implementations of the Multivariate Decomposition Method (MDM) for approximating integrals of $ınfty$-variate functions. Such $ınfty$-variate integrals occur for example as expectations in uncertainty quantification. Starting with the anchored decomposition $f = \sum_u\subsetN f_u$, where the sum is over all finite subsets of $N$ and each $f_u$ depends only on the variables $x_j$ with $jınu$, our MDM algorithm approximates the integral of $f$ by first truncating the sum to some `active set' and then approximating the integral of the remaining functions $f_u$ term-by-term using Smolyak or (randomized) quasi-Monte Carlo (QMC) quadratures. The anchored decomposition allows us to compute $f_u$ explicitly by function evaluations of $f$. Given the specification of the active set and theoretically derived parameters of the quadrature rules, we exploit structures in both the formula for computing $f_u$ and the quadrature rules to develop computationally efficient strategies to implement the MDM in various scenarios. In particular, we avoid repeated function evaluations at the same point. We provide numerical results for a test function to demonstrate the effectiveness of the algorithm.

@preprint{gilbert2017efficient, abstract = {In this paper we focus on efficient implementations of the Multivariate Decomposition Method (MDM) for approximating integrals of $\infty$-variate functions. Such $\infty$-variate integrals occur for example as expectations in uncertainty quantification. Starting with the anchored decomposition $f = \sum_{\mathfrak{u}\subset\mathbb{N}} f_\mathfrak{u}$, where the sum is over all finite subsets of $\mathbb{N}$ and each $f_\mathfrak{u}$ depends only on the variables $x_j$ with $j\in\mathfrak{u}$, our MDM algorithm approximates the integral of $f$ by first truncating the sum to some `active set' and then approximating the integral of the remaining functions $f_\mathfrak{u}$ term-by-term using Smolyak or (randomized) quasi-Monte Carlo (QMC) quadratures. The anchored decomposition allows us to compute $f_\mathfrak{u}$ explicitly by function evaluations of $f$. Given the specification of the active set and theoretically derived parameters of the quadrature rules, we exploit structures in both the formula for computing $f_\mathfrak{u}$ and the quadrature rules to develop computationally efficient strategies to implement the MDM in various scenarios. In particular, we avoid repeated function evaluations at the same point. We provide numerical results for a test function to demonstrate the effectiveness of the algorithm.}, added-at = {2018-06-11T19:12:51.000+0200}, author = {Gilbert, Alexander D. and Kuo, Frances Y. and Nuyens, Dirk and Wasilkowski, Grzegorz W.}, biburl = {https://www.bibsonomy.org/bibtex/2e88ffd8481b4a8c0230c4b1898f379b5/tobydriscoll}, description = {Efficient implementations of the Multivariate Decomposition Method for approximating infinite-variate integrals}, interhash = {dbd90e0b604f67286a7458b9febb6c26}, intrahash = {e88ffd8481b4a8c0230c4b1898f379b5}, keywords = {approximation quadrature}, note = {cite arxiv:1712.06782}, timestamp = {2018-06-11T19:12:51.000+0200}, title = {Efficient implementations of the Multivariate Decomposition Method for approximating infinite-variate integrals}, url = {http://arxiv.org/abs/1712.06782}, year = 2017 }