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Title: An algorithm for QMC integration using low-discrepancy lattice sets (English)
Author: Franěk, Vojtěch
Language: English
Journal: Commentationes Mathematicae Universitatis Carolinae
ISSN: 0010-2628 (print)
ISSN: 1213-7243 (online)
Volume: 49
Issue: 3
Year: 2008
Pages: 447-462
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Category: math
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Summary: Many low-discrepancy sets are suitable for quasi-Monte Carlo integration. Skriganov showed that the intersections of suitable lattices with the unit cube have low discrepancy. We introduce an algorithm based on linear programming which scales any given lattice appropriately and computes its intersection with the unit cube. We compare the quality of numerical integration using these low-discrepancy lattice sets with approximations using other known (quasi-)Monte Carlo methods. The comparison is based on several numerical experiments, where we consider both the precision of the approximation and the speed of generating the sets. We conclude that up to dimensions about 15, low-discrepancy lattices yield fairly good results. In higher dimensions, our implementation of the computation of the intersection takes too long and ceases to be feasible. (English)
Keyword: QMC integration
Keyword: lattices
Keyword: discrepancy
MSC: 65C05
MSC: 65D30
idZBL: Zbl 1212.65008
idMR: MR2490439
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Date available: 2009-05-05T17:12:19Z
Last updated: 2013-09-22
Stable URL: http://hdl.handle.net/10338.dmlcz/119735
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Reference: [1] Beck J., Chen W.L.: Irregularities of Distribution.Cambridge University Press, Cambridge, 1987. Zbl 1156.11029, MR 0903025
Reference: [2] Cranley R., Patterson T.N.L.: Randomization of number theoretic methods for multiple integration.SIAM J. Numer. Anal. 13 (1976), 6 909-914. Zbl 0354.65016, MR 0494820, 10.1137/0713071
Reference: [3] Davis P.J., Rabinowitz P.: Methods of Numerical Integration.2nd edition, Academic Press Inc., Orlando FL, 1984. Zbl 1139.65016, MR 0760629
Reference: [4] Faure H.: Discrepancy of sequences associated with a number system (in dimension $s$).Acta Arith. 41 (1982), 4 337-351. MR 0677547
Reference: [5] Genz A.: Testing multidimensional integration routines.in Tools, Methods and Languages for Scientific and Engineering Computation, B. Ford, J. C. Rault and F. Thomasset, Eds., North-Holland, Amsterdam, 1984.
Reference: [6] Hua L.K., Wang Y.: Applications of Number Theory to Numerical Analysis.Springer, Berlin, 1981. Zbl 0465.10045, MR 0617192
Reference: [7] Janse van Rensburg E.J., Torrie G.M.: Estimation of multidimensional integrals: is Monte Carlo the best method?.J. Phys. A 26 (1993), 943-953. Zbl 0781.65015, MR 1211087, 10.1088/0305-4470/26/4/022
Reference: [8] L'Ecuyer P., Lemieux C.: Variance reduction via lattice rules.in Management Science 49-6 (2000), 1214-1235.
Reference: [9] Lovász L.: An algorithmic theory of numbers, graphs and convexity.CBMS-NSF Regional Conference Series in Applied Mathematics 50, SIAM, Philadelphia, Pennsylvania, 1986. MR 0861822
Reference: [10] Matoušek J.: Geometric Discrepancy: An Illustrated Guide.Springer, Berlin, 1999. MR 1697825
Reference: [11] Matoušek J.: On the $L_2$-discrepancy for anchored boxes.J. Complexity 14 (1998), 527-556. MR 1659004, 10.1006/jcom.1998.0489
Reference: [12] Niederreiter H.: Random number generation and quasi-Monte Carlo methods.CBMS-NSF Regional Conference Series in Applied Mathematics 63, SIAM, Philadelphia, Pennsylvania, 1992. Zbl 0761.65002, MR 1172997
Reference: [13] Owen A.B.: Randomly permuted $(t,m,s)$-nets and $(t,s)$-sequences.in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Harald Niederreiter and Peter Jau-Shyong Shiue, Eds., Springer, New York, 1995, pp. 299-317. Zbl 0831.65024, MR 1445791
Reference: [14] Owen A.B.: Scrambled net variance for integrals of smooth functions.Ann. Statist. 25 (1997), 4 1541-1562. MR 1463564, 10.1214/aos/1031594731
Reference: [15] Owen A.B.: Monte-Carlo variance of scrambled net quadrature.SIAM J. Numer. Anal. 34 (1997), 5 1884-1910. Zbl 0890.65023, MR 1472202, 10.1137/S0036142994277468
Reference: [16] Radovic I., Sobol' I.M., Tichy R.F.: Quasi-Monte Carlo methods for numerical integration: Comparison of different low-discrepancy sequences.Monte Carlo Methods Appl. 2 (1996), 1-14. Zbl 0851.65015, MR 1395313
Reference: [17] Roos P., Arnold L.: Numerische Experimente zur mehrdimensionalen Quadratur.Österreich. Akad. Wiss. Math.-Natur. Kl. S.-B. II 172 (1963), 271-286. Zbl 0128.36902, MR 0170475
Reference: [18] Skriganov M.M.: Constructions of uniform distributions in terms of geometry of numbers.Algebra i Analiz 6 (1994), 200-230. Zbl 0840.11041, MR 1301838
Reference: [19] Sloan I.H., Joe S.: Lattice Method for Multiple Integration.Clarendon Press, Oxford University Press, New York, 1994. MR 1442955
Reference: [20] Sloan I.H., Kuo F.Y., Joe S.: Constructing randomly shifted lattice rules in weighted Sobolev spaces.SIAM J. Numer. Anal. 40 (2002), 1650-1665. Zbl 1037.65005, MR 1950616, 10.1137/S0036142901393942
Reference: [21] Sloan I.H., Kuo F.Y., Joe S.: On the step-by-step construction of quasi-Monte Carlo integration rules that achieve strong tractability error bounds in weighted Sobolev spaces.Math. Comp. 71 (2002), 1609-1640. Zbl 1011.65001, MR 1933047, 10.1090/S0025-5718-02-01420-5
Reference: [22] Spanier J., Maize E.H.: Quasi-random methods for estimating integrals using relatively small samples.SIAM Review 36 1 (1994), 18-44. Zbl 0824.65009, MR 1267048, 10.1137/1036002
Reference: [23] Tezuka S.: Uniform Random Numbers. Theory and Practice.Kluwer Academic Publishers, Dordrecht, 1995. Zbl 0841.65004
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