*This work was originally published here, and has been reproduced with the author’s permission.*

The first blog post in this series, “Why add Q to MC?”, introduced the concept of evenly spread points, which are commonly referred to as *low discrepancy* (LD) points. This is in contrast to independent and identically distributed (IID) points.

Consider two sequences,

\(\boldsymbol{T}_1, \boldsymbol{T}_2, \ldots \overset{\text{IID}}{\sim} \mathcal{U}[0,1]^d\)

\[\boldsymbol{X}_1, \boldsymbol{X}_2, \ldots \overset{\text{LD}}{\sim} \mathcal{U}[0,1]^d.\]

Both sequences are expected to look like points spread uniformly over the unit cube, \([0,1]^d\). The first sequence must be random, or as random looking as our (deterministic) random number generators can make it. Since the points are independent, the location of any \(\boldsymbol{T}_i\) has no bearing on the location of any other \(\boldsymbol{T}_j\). Removing a point at random does not affect the IID property.

The second sequence may be random or deterministic. Let \(F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}}\) denote the empirical distribution function of the first \(n\) points of this sequence, i.e., the probability distribution that assigns a probability of \(1/n\) to each location \(\boldsymbol{X}_i\). For \(\boldsymbol{X}_1, \boldsymbol{X}_2, \ldots\) to be LD, \(F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}}\) should be close to the uniform probability distribution, \(F_{\text{unif}}: \boldsymbol{x} \mapsto x_1 \cdots x_d\).

“Close” implies that we can measure how far apart two distributions are. We call this measure the *discrepancy*. Just like beauty is in the eye of the beholder, so there are different measurements of discrepancy. They tend to take the form of a the distance between the empirical distribution of the point set, \(F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}}\), and the target measure, \(F_{\text{unif}}\). An example is the star discrepancy [1,(3.16)]:

\[\text{disc}(\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}) := \sup_{\boldsymbol{x} \in [0,1]^d} | F_{\text{unif}} (\boldsymbol{x}) – F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}}(\boldsymbol{x})|,\]

which is known in the statistics literature as a Kolomogorov-Smirnov goodness-of-fit statistic. This discrepancy compares is the maximum absolute difference between the volume of the box \([\boldsymbol{0},\boldsymbol{x}]^d\) and the proportion of the points \(\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}\) that lie in that box. Ideally, these should be the same, but practically they will be at least a bit different.

The computational cost of evaluating the star discrepancy can be rather large, typically at least \(\mathcal{O}(n^d)\) operations. A family of computationally cheaper discrepancies is defined in terms of kernel, \(K: [0,1]^d \times [0,1]^d \to \mathbb{R}\), which satisfies two crucial properties:

\[\begin{aligned} \text{Symmetry:} \quad& K(\boldsymbol{t},\boldsymbol{x}) = K(\boldsymbol{x},\boldsymbol{t}) \qquad \forall \boldsymbol{t}, \boldsymbol{x} \in [0,1]^d, \\ \text{Positive Definiteness:} \quad& \boldsymbol{c}^T \mathsf{K} \boldsymbol{c} > 0, \text{ where } \mathsf{K} =\bigl(K(\boldsymbol{x}_i, \boldsymbol{x}_j) \bigr)_{i,j=1}^n, \\ & \qquad \qquad \forall \boldsymbol{c} \ne \boldsymbol{0}, \text{ distinct } \boldsymbol{x}_1, \boldsymbol{x}_2, \ldots \in [0,1]^d. \end{aligned}\]

For such a kernel, we may define a discrepancy as

\[\begin{aligned} &\text{disc}(\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}) \\ &\qquad := \int_{[0,1]^d \times [0,1]^d} K(\boldsymbol{t}, \boldsymbol{x}) \, \rm d (F_{\text{unif}} – F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}})(\boldsymbol{t}) \, \rm d(F_{\text{unif}} – F_{\{\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n\}}) (\boldsymbol{x}) \\ &\qquad = \int_{[0,1]^d \times [0,1]^d} K(\boldsymbol{t}, \boldsymbol{x}) \, \rm d \boldsymbol{t} \rm d \boldsymbol{x} – \frac{2}{n} \sum_{i=1}^n \int_{[0,1]^d} K(\boldsymbol{x}_i,\boldsymbol{x}) \, \rm d \boldsymbol{x} \\ &\qquad \qquad \qquad + \frac{1}{n^2} \sum_{i,j=1}^n K(\boldsymbol{x}_i, \boldsymbol{x}_j). \end{aligned}\]

For example, the centered $L^2$-discrepancy [2] is defined in terms of the kernel

\[\begin{aligned} K(\boldsymbol{t},\boldsymbol{x}) = \prod_{k=1}^d \left[1 + \frac{1}{2} |t_k – 1/2| + \frac{1}{2} |x_k – 1/2| = \frac{1}{2} |t_k – x_k| \right]. \end{aligned}\]

After straightforward calculations it becomes

\[\begin{aligned} & \text{disc}({\boldsymbol{X}_1, \ldots, \boldsymbol{X}_n}) = \left(\frac{13}{12} \right)^d – \frac{2}{n} \sum_{i=1}^n \prod_{k=1}^d \left(1 + \frac{1}{2} |x_k – 1/2| – \frac{1}{2} |x_k – 1/2|^2 \right)\ + \\ & \qquad \qquad \frac{1}{n^2} \sum_{i,j=1}^n\prod_{k=1}^d \left[1 + \frac{1}{2} |x_{ik} – 1/2| + \frac{1}{2} |x_{jk} – 1/2| = \frac{1}{2} |x_{ik} – x_{jk}| \right]. \end{aligned}\]

This discrepancy only requires \(\mathcal{O}(dn^2)\) operations to evaluate.

LD sequences have discrepancies of \(\mathcal{O}(n^{-1+\epsilon})\) for the discrepancies illustrated above. IID sequences have root mean square discrepancies of \(\mathcal{O}(n^{-1/2})\).This difference is asymptotic order can translate into orders of magnitude improvements in the accuracy of numerical solutions.

For problems where $d$ is large, the discrepancies defined above do not decay so quickly. However, if these discrepancy definitions are modified to include *coordinate weights* [1, Section 4], then they retain their \(\mathcal{O}(n^{-1 +\epsilon})\) decay. Coordinate weights express the assumption that certain coordinates contribute more to the variation of the function than others.

Demonstrating that a particular sequence is LD can be done by brute force computation, which requires in general \(\mathcal{O}(dn^2)\) operations. For certain sequences matched with certain discrepancy definitions, this can be reduced to \(\mathcal{O}(dn)\) operations. If \(n\) is small enough, the search for an LD set can be performed using global optimization algorithms [3]. Number theoretic arguments are used to construct certain popular LD sequences [4,5].

#### References

1. Dick, J., Kuo, F. & Sloan, I. H. High dimensional integration — the Quasi-Monte Carlo way. Acta Numer.22, 133–288 (2013).

2. Hickernell, F. J. A Generalized Discrepancy and Quadrature Error Bound. Math. Comp. 67, 299–322 (1998).

3. Winker, P. & Fang, K. T. Application of Threshold Accepting to the Evaluation of the Discrepancy of a Set of Points. SIAM J. Numer. Anal. 34, 2028–2042 (1997).

4. Dick, J. & Pillichshammer, F. Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration (Cambridge University Press, Cambridge, 2010).

5. Niederreiter, H. Random Number Generation and Quasi-Monte Carlo Methods (SIAM, Philadelphia, 1992).