Low-discrepancy sequence

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In mathematics, a low-discrepancy sequence is a sequence with the property that for all N, the subsequence x1, ..., xN is almost uniformly distributed (in a sense to be made precise), and x1, ..., xN+1 is almost uniformly distributed as well.

Low-discrepancy sequences are also called quasi-random or sub-random sequences, due to their use in situations similar to those when pseudorandom or random numbers are used instead. The "quasi" modifier is used to denote more clearly that the numbers are not random (and to differentiate them from pseudorandomness, which uses different assumptions), but have useful properties similar to randomness in certain applications such as the quasi-Monte Carlo method.

The notion of uniformity is made precise as the discrepancy defined below. Roughly speaking, the discrepancy of a sequence is low if the number of points falling into a set B is close to the number one would expect from the measure of B.

At least three methods of numerical integration can be phrased as follows. Given a set x1, ..., xN in the interval [0,1], approximate the integral of a function f as the average of the function evaluated at those points:

\int_0^1 f(u)\,du \approx \frac{1}{N}\,\sum_{i=1}^N f(x_i).

If the points are chosen as xi = i/N, this is the rectangle rule. If the points are chosen to be randomly (or pseudorandomly) distributed, this is the Monte Carlo method. If the points are chosen as elements of a low-discrepancy sequence, this is the quasi-Monte Carlo method. A remarkable result, the Koksma-Hlawka inequality, shows that the error of such a method can be bounded by the product of two terms, one of which depends only on f, and another which is the discrepancy of the set x1, ..., xN. The Koksma-Hlawka inequality is stated below.

It is convenient to construct the set x1, ..., xN in such a way that if a set with N+1 elements is constructed, the previous N elements need not be recomputed. The rectangle rule uses points set which have low discrepancy, but in general the elements must be recomputed if N is increased. Elements need not be recomputed in the Monte Carlo method if N is increased, but the point sets do not have minimal discrepancy. By using low-discrepancy sequences, the quasi-Monte Carlo method has the desirable features of the other two methods.

Contents

[edit] Definition of discrepancy

The Star-Discrepancy is defined as follows, using Niederreiter's notation.

D^*_N(P) = \sup_{B\in J^*} \left| \frac{A(B;P)}{N} - \lambda_s(B) \right|

where P is the set x1, ..., xN, λs is the s-dimensional Lebesgue measure, A(B;P) is the number of points in P that fall into B, and J* is the set of intervals of the form

\prod_{i=1}^s [0, u_i)

where ui is in the half-open interval [0, 1). Therefore

D^*_N(P) =\|{\rm disc}\|_\infty

where the discrepancy function is defined by

{\rm disc}(y)=\frac{A([0,y);P)}{N}-\lambda_s([0,y)).

[edit] Graphical examples

The points plotted below are the first 100, 1000, and 10000 elements in a sequence of the Sobol type. For comparison, 10000 elements of a sequence of pseudorandom points are also shown.

The low-discrepancy sequence was generated by TOMS algorithm 659, described by P. Bratley and B.L. Fox in ACM Transactions on Mathematical Software, vol. 14, no. 1, pp 88--100. An implementation of the algorithm in Fortran may be downloaded from Netlib, URL: http://www.netlib.org/toms/659

The first 100 points in a low-discrepancy sequence of the Sobol type.
The first 100 points in a low-discrepancy sequence of the Sobol type.
The first 1000 points in the same sequence. These 1000 comprise the first 100, with 900 more points.
The first 1000 points in the same sequence. These 1000 comprise the first 100, with 900 more points.
The first 10000 points in the same sequence. These 10000 comprise the first 1000, with 9000 more points.
The first 10000 points in the same sequence. These 10000 comprise the first 1000, with 9000 more points.
For comparison, here are the first 10000 points in a sequence of uniformly distributed pseudorandom numbers. Regions of higher and lower density are evident.
For comparison, here are the first 10000 points in a sequence of uniformly distributed pseudorandom numbers. Regions of higher and lower density are evident.



[edit] The Koksma-Hlawka inequality

Let Īs be the s-dimensional unit cube, Īs = [0, 1] × ... × [0, 1]. Let f have bounded variation V(f) on Īs in the sense of Hardy and Krause. Then for any x1, ..., xN in Is = [0, 1) × ... × [0, 1),

\left| \frac{1}{N} \sum_{i=1}^N f(x_i) - \int_{\bar I^s} f(u)\,du \right| \le V(f)\, D_N^* (x_1,\ldots,x_N).

The Koksma Hlawka inequality is sharp in the following sense:

For any point set x1,...,xN in Is and any

ε > 0,

there is a function f with bounded variation and V(f)=1 such that

\left| \frac{1}{N} \sum_{i=1}^N f(x_i) - \int_{\bar I^s} f(u)\,du \right|>D_{N}^{*}(x_1,\ldots,x_N)-\epsilon.

Therefore, the quality of a numerical integration rule depends only on the discrepancy D*N(x1,...,xN).

[edit] The formula of Hlawka-Zaremba

Let D=\{1,2,\ldots,d\}. For \emptyset\neq u\subseteq D we write

dx_u:=\prod_{j\in u} dx_j

and denote by (xu,1) the point obtained from x by replacing the coordinates not in u by 1. Then

\frac{1}{N} \sum_{i=1}^N f(x_i) - \int_{\bar I^s} f(u)\,du= \sum_{\emptyset\neq u\subseteq D}(-1)^{|u|} \int_{[0,1]^{|u|}}{\rm disc}(x_u,1)\frac{\partial^{|u|}}{\partial x_u}f(x_u,1) dx_u.

[edit] The L2 version of the Koksma-Hlawka inequality

Applying the Cauchy-Schwarz inequality for integrals and sums to the Hlawka-Zaremba identity, we obtain an L2 version of the Koksma-Hlawka inequality:

\left|\frac{1}{N} \sum_{i=1}^N f(x_i) - \int_{\bar I^s} f(u)\,du\right|\le \|f\|_{d}\,{\rm disc}_{d}(\{t_i\}),

where

{\rm disc}_{d}(\{t_i\})=\left(\sum_{\emptyset\neq u\subseteq D} \int_{[0,1]^{|u|}}{\rm disc}(x_u,1)^2 dx_u\right)^{1/2}

and

\|f\|_{d}=\left(\sum_{u\subseteq D} \int_{[0,1]^{|u|}} \left|\frac{\partial^{|u|}}{\partial x_u}f(x_u,1)\right|^2 dx_u\right)^{1/2}.

[edit] The Erdős-Turan-Koksma inequality

It is computationally hard to find the exact value of the discrepancy of large point sets. The Paul Erdős-Turán-Koksma inequality provides an upper bound.

Let x1,...,xN be points in Is and H be an arbitrary positive integer. Then

D_{N}^{*}(x_1,\ldots,x_N)\leq \left(\frac{3}{2}\right)^s \left( \frac{2}{H+1}+ \sum_{0<\|h\|_{\infty}\leq H}\frac{1}{r(h)} \left| \frac{1}{N} \sum_{n=1}^{N} e^{2\pi i\langle h,x_n\rangle} \right| \right)

where

r(h)=\prod_{i=1}^s\max\{1,|h_i|\}\quad\mbox{for}\quad h=(h_1,\ldots,h_s)\in\Z^s.

[edit] The main conjectures

Conjecture 1. There is a constant cs depending only on s, such that

D_{N}^{*}(x_1,\ldots,x_N)\geq c_s\frac{(\ln N)^{s-1}}{N}

for any finite point set x1,...,xN.

Conjecture 2. There is a constant c's depending only on s, such that

D_{N}^{*}(x_1,\ldots,x_N)\geq c'_s\frac{(\ln N)^{s}}{N}

for any infinite sequence x1,x2,x3,....

These conjectures are equivalent. They have been proved for s ≤ 2 by W. M. Schmidt. In higher dimensions, the corresponding problem is still open. The best-known lower bounds are due to K. F. Roth.

[edit] The best-known sequences

Constructions of sequences are known (due to Faure, Halton, Hammersley, Sobol, Niederreiter and van der Corput) such that

D_{N}^{*}(x_1,\ldots,x_N)\leq C\frac{(\ln N)^{s}}{N}.

where C is a certain constant, depending of the sequence. After Conjecture 2., these sequences are believed to have the best possible order of convergence. See also: Halton sequences.

[edit] Lower bounds

Let s = 1. Then

D_N^*(x_1,\ldots,x_N)\geq\frac{1}{2N}

for any finite point set x1, ..., xN.

Let s = 2. W. M. Schmidt proved that for any finite point set x1, ..., xN,

D_N^*(x_1,\ldots,x_N)\geq C\frac{\log N}{N}

where

C=\max_{a\geq3}\frac{1}{16}\frac{a-2}{a\log a}=0.02333...

For arbitrary dimensions s > 1, K.F. Roth proved that

D_N^*(x_1,\ldots,x_N)\geq\frac{1}{2^{4s}}\frac{1}{((s-1)\log2)^\frac{s-1}{2}}\frac{\log^{\frac{s-1}{2}}N}{N}

for any finite point set x1, ..., xN. This bound is the best known for s > 3.

[edit] Applications

[edit] References

  • Harald Niederreiter. Random Number Generation and Quasi-Monte Carlo Methods. Society for Industrial and Applied Mathematics, 1992. ISBN 0-89871-295-5
  • Michael Drmota and Robert F. Tichy, Sequences, discrepancies and applications, Lecture Notes in Math., 1651, Springer, Berlin, 1997, ISBN 3-540-62606-9
  • William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T. Vetterling. Numerical Recipes in C. Cambridge, UK: Cambridge University Press, second edition 1992. ISBN 0-521-43108-5 (see Section 7.7 for a less technical discussion of low-discrepancy sequences)

[edit] External links

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