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A New Quasi-Monte Carlo Technique Based on Nonnegative Least Squares and Approximate Fekete Points

Published online by Cambridge University Press:  17 November 2016

Claudia Bittante*
Affiliation:
Department of Mathematics, University of Padova, Italy
Stefano De Marchi*
Affiliation:
Department of Mathematics, University of Padova, Italy
Giacomo Elefante*
Affiliation:
Department of Mathematics, University of Fribourg, Switzerland
*
*Corresponding author. Email addresses:cbittant@math.unipd.it (C. Bittante), demarchi@math.unipd.it (S. De Marchi), giacomo.elefante@unifr.ch (G. Elefante)
*Corresponding author. Email addresses:cbittant@math.unipd.it (C. Bittante), demarchi@math.unipd.it (S. De Marchi), giacomo.elefante@unifr.ch (G. Elefante)
*Corresponding author. Email addresses:cbittant@math.unipd.it (C. Bittante), demarchi@math.unipd.it (S. De Marchi), giacomo.elefante@unifr.ch (G. Elefante)
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Abstract

The computation of integrals in higher dimensions and on general domains, when no explicit cubature rules are known, can be ”easily” addressed by means of the quasi-Monte Carlo method. The method, simple in its formulation, becomes computationally inefficient when the space dimension is growing and the integration domain is particularly complex. In this paper we present two new approaches to the quasi-Monte Carlo method for cubature based on nonnegative least squares and approximate Fekete points. The main idea is to use less points and especially good points for solving the system of the moments. Good points are here intended as points with good interpolation properties, due to the strict connection between interpolation and cubature. Numerical experiments show that, in average, just a tenth of the points should be used mantaining the same approximation order of the quasi-Monte Carlo method. The method has been satisfactory applied to 2 and 3-dimensional problems on quite complex domains.

Type
Research Article
Copyright
Copyright © Global-Science Press 2016 

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References

[1] Bittante, C., Una nuova tecnica di cubatura quasi-Monte Carlo su domini 2D e 3D, (in Italian), Master's thesis, University of Padua, March 2014.Google Scholar
[2] Briani, M., Sommariva, A., Vianello, M., Computing Fekete and Lebesgue points: simplex, square, disk, J. Comput. Appl. Math. 236 (2012), no. 9, 24772486.CrossRefGoogle Scholar
[3] Bos, L., Calvi, J.-P., Levenberg, N., Sommariva, A. and Vianello, M., Geometric weakly admissible meshes, discrete least squares approximation and approximate Fekete points, Math. Comp. 80(275) (2011), 16231638.Google Scholar
[4] Bos, L., De Marchi, S., Sommariva, A. and Vianello, M., Computing multivariate Fekete and Leja points by numerical linear algebra, SIAM J. Num. Anal. Vol. 48(5) (2010), 19841999.Google Scholar
[5] Bos, L., De Marchi, S., Sommariva, A. and Vianello, M., Weakly Admissible Meshes and Discrete Extremal Sets, Numer. Math. Theor. Meth. Appl. Vol. 4(1) (2011), 112.CrossRefGoogle Scholar
[6] Bojanov, B. and Petrova, G., Numerical integration over a disc. A new Gaussian quadrature formula, Numer. Math. 80 (1998), 3959.Google Scholar
[7] Bos, L. and Vianello, M., Low cardinality admissible meshes on quadrangles, triangles and disks, Math. Inequal. Appl. 15 (2012), 229235.Google Scholar
[8] Caflisch, R. E., Monte Carlo and quasi-Monte Carlo methods, Acta Numerica vol. 7, Cambridge University Press (1998), 149.Google Scholar
[9] Caliari, M., De Marchi, S. and Vianello, M., Bivariate polynomial interpolation on the square at new nodal sets, Appl. Math. Comput. 165(2) (2005), 261274 Google Scholar
[10] Calvi, J. P. and Levenberg, N., Uniform approximation by discrete least squares polynomials, J. Approx. Theory 152 (2008), 82100.Google Scholar
[11] Civril, A. and Magdon-Ismail, M., On selecting a maximum volume sub-matrix of a matrix and related problems, Theoretical Computer Science 410 (2009), 48014811.CrossRefGoogle Scholar
[12] Da Fies, G. and Vianello, M., Agebraic cubature on planar lenses and bubbles, Dolomites Res. Notes Approx. 5 (2012), 712.Google Scholar
[13] De Marchi, S., Marchiori, M. and Sommariva, A., Polynomial approximation and cubature at approximate Fekete and Leja points of the cylinder, Appl. Math. Comput. Vol. 218(21) (2012), 1061710629.Google Scholar
[14] De Marchi, S. and Vianello, M., Polynomial approximation on pyramids, cones and solids of rotation, Dolomites Res. Notes Approx, Proceedings DWCAA12, Vol. 6 (2013), 2026.Google Scholar
[15] Dick, J. and Pillichshammer, F., Digital Nets and Sequences. Discrepancy Theory and Quasi-Monte Carlo Integration, Cambridge University Press, Cambridge, 2010.Google Scholar
[16] Drmota, M. and Tichy, R. F., Sequences, discrepancies and applications, Lecture Notes in Math., 1651, Springer, Berlin, 1997.Google Scholar
[17] Klenke, A., Probability Theory: A Comprehensive Course., Springer-Verlag London, 2014.Google Scholar
[18] Kroó, A., On optimal polynomial meshes, J. Approx. Theory 163 (2011), 11071124.Google Scholar
[19] Lawson, C. L. and Hanson, R. J., Solving Least Squares Problems, Prentice-0Hall 1974, p. 161.Google Scholar
[20] Lemieux, C., Monte Carlo and Quasi-Monte Carlo Sampling, Springer 2009.Google Scholar
[21] Morrow, C. R. and Patterson, T. N. L., Construction of algebraic cubatures rules using polynomial ideal theory, SIAM J. Numer. Anal. 15 (1978), 953976.Google Scholar
[22] Morokoff, W. J. and Caflisch, R. E., Quasi-random sequences and their discrepancies, SIAM J. Sci. Comput. 15 (1994), no. 6, 12511279.CrossRefGoogle Scholar
[23] Niederreiter, H., Random Number Generation and Quasi-Monte Carlo Methods., SIAM, 1992.Google Scholar
[24] Niederreiter, H. G., Quasi-Monte Carlo methods and pseudo-random numbers, Bull. Amer. Math. Soc. 84 (1978), no. 6, 9571041.Google Scholar
[25] Owen, A. B., Multidimensional variation for quasi-Monte Carlo, http://finmath.stanford.edu/~owen/reports/ktfang.pdf.Google Scholar
[26] Piazzon, F., Vianello, M., Analytic transformations of admissible meshes, East J. Approx. 16 (2010), 389398.Google Scholar
[27] Sommariva, A., Vianello, M., Product Gauss cubature over polygons based on Green's integration formula, BIT Num. Mathematics 47 (2007), 441453.Google Scholar
[28] Sommariva, A., Vianello, M., Approximate Fekete points for weighted polynomial interpolation, Electron. Trans. Numer. Anal. 37 (2010), 122.Google Scholar
[29] Sommariva, A., Vianello, M., Compression of multivariate discrete measures and applications, Numer. Funct. Anal. Optim. 36 (2015), 11981223.Google Scholar
[30] Santin, G., Sommariva, A., Vianello, M., An algebraic cubature formula on curvilinear polygons. Appl. Math. Comput. 217 (2011), 1000310015.Google Scholar
[31] Sommariva, A., Vianello, M., ChebfunGauss: Matlab code for Gauss-Green cubature by the Chebfun package, available at http://www.math.unipd.it/~marcov/CAAsoft.html Google Scholar
[32] Sommariva, A. and Vianello, M., Gauss-Green cubature and moment computation over arbitrary geometries, J. Comput. Appl. Math. 231 (2009), 886896.Google Scholar
[33] Strauch, O. and Porubský, Š., Distribution of Sequences: A Sampler, Peter Lang Publishing House, Frankfurt am Main 2005.Google Scholar
[34] Tuffin, B., Radomization of quasi-Monte Carlo methods for error estimation: survey and normal approximation, Monte Carlo Methods and Applications, 10(3-4) (2008), 617628.Google Scholar