Abstract
We provide a framework for the sparse approximation of multilinear problems and show that several problems in uncertainty quantification fit within this framework. In these problems, the value of a multilinear map has to be approximated using approximations of different accuracy and computational work of the arguments of this map. We propose and analyze a generalized version of Smolyak’s algorithm, which provides sparse approximation formulas with convergence rates that mitigate the curse of dimension that appears in multilinear approximation problems with a large number of arguments. We apply the general framework to response surface approximation and optimization under uncertainty for parametric partial differential equations using kernel-based approximation. The theoretical results are supplemented by numerical experiments.
Similar content being viewed by others
References
Alexanderian, A., Petra, N., Stadler, G., Ghattas, O.: Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations. arXiv:1602.07592 (2016)
Aronszajn, N.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337–404 (1950)
Chkifa, A., Cohen, A., Schwab, C.: Breaking the curse of dimensionality in sparse polynomial approximation of parametric PDEs. J. Math. Pures Appl. 103(2), 400–428 (2015)
Dereich, S., Mueller-Gronbach, T.: General multilevel adaptations for stochastic approximation algorithms. arXiv:1506.0548 (2015)
Dong, Z., Georgoulis, E.H., Levesley, J., Usta, F.: Fast multilevel sparse Gaussian kernels for high-dimensional approximation and integration. arXiv:1501.03296 (2015)
Dung, D.: Continuous algorithms in n-term approximation and non-linear widths. J. Approx. Theory 102(2), 217–242 (2000)
Fasshauer, G., McCourt, M.: Kernel-Based Approximation Methods Using MATLAB. World Scientific, Singapore (2016)
Georgoulis, E.H., Levesley, J., Subhan, F.: Multilevel sparse kernel-based interpolation. SIAM J. Sci. Comput. 35(2), A815–A831 (2013)
Gerstner, T., Griebel, M.: Numerical integration using sparse grids. Numer. Algorithms 18(3–4), 209–232 (1998)
Gerstner, T., Heinz, S.: Dimension-and time-adaptive multilevel Monte Carlo methods. In: Sparse Grids and Applications, pp. 107–120. Springer (2012)
Giles, M.B.: Multilevel Monte Carlo path simulation. Oper. Res. 56(3), 607–617 (2008)
Griebel, M., Oettershagen, J.: On tensor product approximation of analytic functions. J. Approx. Theory 207, 348–379 (2016)
Griebel, M., Harbrecht, H.: A note on the construction of L-fold sparse tensor product spaces. Constr. Approx. 38(2), 235–251 (2013)
Griebel, M., Harbrecht, H.: On the construction of sparse tensor product spaces. Math. Comput. 82(282), 975–994 (2013)
Griebel, M., Schneider, M., Zenger, C.: A combination technique for the solution of sparse grid problems. In: de Groen, P., Beauwens, R. (eds.) Iterative Methods in Linear Algebra, pp. 263–281. Elsevier, Amsterdam (1992)
Hackbusch, W.: Tensor Spaces and Numerical Tensor Calculus. Springer, New York (2012)
Haji-Ali, A.-L., Nobile, F., Tamellini, L., Tempone, R.: Multi-index Stochastic collocation convergence rates for random PDEs with parametric regularity. Found. Comput. Math. 16(6), 1555–1605 (2016)
Haji-Ali, A.-L., Nobile, F., Tamellini, L., Tempone, R.: Multi-index stochastic collocation for random PDEs. Comput. Method. Appl. Mech. Eng. 306, 95–122 (2016)
Harbrecht, H., Peters, M., Siebenmorgen, M.: Multilevel accelerated quadrature for PDEs with log-normally distributed diffusion coefficient. SIAM/ASA J. Uncertain. Quantif. 4(1), 520–551 (2016)
Harbrecht, H., Peters, M., Siebenmorgen M.: On multilevel quadrature for elliptic stochastic partial differential equations. In: Sparse Grids and Applications, pp. 161–179. Springer, New York (2012)
Heinrich, S.: Multilevel Monte Carlo methods. In: International Conference on Large-Scale Scientific Computing, pp. 58–67. Springer, New York (2001)
Kuo, F.Y., Schwab, C., Sloan, I.H.: Quasi-Monte Carlo finite element methods for a class of elliptic partial differential equations with random coefficients. SIAM J. Numer. Anal. 50(6), 3351–3374 (2012)
Kuo, F.Y., Scheichl, R., Schwab, C., Sloan, I.H., Ullmann, E.: Multilevel Quasi-Monte Carlo methods for lognormal diffusion problems. Math. Comput. 86(308), 2827–2860 (2017)
Nirenberg, L.: An extended interpolation inequality. Ann. Scuola Norm. Sci. 20(4), 733–737 (1966)
Novak, E., Ritter, K.: High dimensional integration of smooth functions over cubes. Numer. Math. 75(1), 79–97 (1996)
Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)
Sahinidis, N.V.: Optimization under uncertainty: state-of-the-art and opportunities. Comput. Chem. Eng. 28(6), 971–983 (2004)
Schaback, R., Wendland, H.: Kernel techniques: from machine learning to meshless methods. Acta Numer. 15(5), 543–639 (2006)
Schreiber, A.: Die Methode von Smolyak bei der multivariaten Interpolation’. PhD thesis. Universität Göttingen (2000)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2001)
Shapiro, A.: Stochastic programming approach to optimization under uncertainty. Math. Program. 112(1), 183–220 (2008)
Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on Stochastic Programming: Modeling and Theory. SIAM, Philadelphia (2014)
Smolyak, S.A.: Quadrature and interpolation formulas for tensor products of certain classes of functions. Soviet Math. Dokl. 4, 240–243 (1963)
Stein, M.L.: Interpolation of spatial data: some theory for kriging. Springer, New York (2012)
Teckentrup, A.L., Jantsch, P., Webster, C.G., Gunzburger, M.: A multilevel stochastic collocation method for partial differential equations with random input data. SIAM/ASA J. Uncertain. Quantif. 3(1), 1046–1074 (2015)
Wahba, G.: Interpolating Surfaces: High Order Convergence Rates and Their Associated Designs, with Application to X-ray Image Reconstruction. Technical report, DTIC (1978)
Wasilkowski, G.W., Wozniakowski, H.: Explicit cost bounds of algorithms for multivariate tensor product problems. J. Complex. 11(1), 1–56 (1995)
Wendland, H.: Scattered Data Approximation. Cambridge University Press, Cambridge (2004)
Wendland, H., Rieger, C.: Approximate interpolation with applications to selecting smoothing parameters. Numer. Math. 101(4), 729–748 (2005)
Acknowledgements
S. Wolfers and R. Tempone are members of the KAUST Strategic Research Initiative, Center for Uncertainty Quantification in Computational Sciences and Engineering. R. Tempone received support from the KAUST CRG3 Award Ref: 2281. F. Nobile received support from the Center for ADvanced MOdeling Science (CADMOS). We thank Abdul-Lateef Haji-Ali for many helpful discussions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nobile, F., Tempone, R. & Wolfers, S. Sparse approximation of multilinear problems with applications to kernel-based methods in UQ. Numer. Math. 139, 247–280 (2018). https://doi.org/10.1007/s00211-017-0932-4
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00211-017-0932-4