Quasi-Monte Carlo (QMC) methods are equal weight quadrature rules to approximate integrals over the unit cube with respect to the uniform measure. In this paper we discuss QMC integration with respect to general product measures defined on an arbitrary cube. We only require that the cumulative distribution function is invertible. We develop a worst-case error bound and study the dependence of the error on the number of points and the dimension for digital nets and sequences as well as polynomial lattice point sets, which are mapped to the domain using the inverse cumulative distribution function. We do not require any smoothness properties of the probability density function and the worst-case error does not depend on the particular choice of density function and its smoothness. The component-by-component construction of polynomial lattice rules is based on a criterion which depends only on the size of the cube but is otherwise independent of the product measure.
Quasi-Monte Carlo methods are a way of improving the efficiency of Monte Carlo methods. Digital nets and sequences are one of the low discrepancy point sets used in quasi-Monte Carlo methods. This thesis presents the three new results pertaining to digital nets and sequences: implementing randomized digital nets, finding the distribution of the discrepancy of scrambled digital nets, and obtaining better quality of digital nets through evolutionary computation. Finally, applications of scrambled and non-scrambled digital nets are provided.
The Mean Square Quasi-Monte Carlo Error for Digitally Shifted Digital Nets
Graduate School of Engineering, The University of Tokyo（东京大学大学院工学系研究科）；Graduate School of Mathematical Sciences, The University of Tokyo（东京大学大学院数理科学研究科）
When a plain Monte Carlo estimate on $n$ samples has variance $σ^2/n$, then scrambled digital nets attain a variance that is $o(1/n)$ as $n\to\infty$. For finite $n$ and an adversarially selected integrand, the variance of a scrambled $(t,m,s)$-net can be at most $Γσ^2/n$ for a maximal gain coefficient $Γ<\infty$. The most widely used digital nets and sequences are those of Sobol'. It was previously known that $Γ\leqslant 2^t3^s$ for Sobol' points as well as Niederreiter-Xing points. In this paper we study nets in base $2$. We show that $Γ\leqslant2^{t+s-1}$ for nets. This bound is a simple, b…
Digital nets are among the most successful methods to construct low-discrepancy point sets for quasi-Monte Carlo integration. Their quality is traditionally assessed by a measure called the $t$-value. A refinement computes the $t$-value of the projections over subsets of coordinates and takes a weighted average (or some other function) of these values. It is also of interest to compute the $t$-values of embedded nets obtained by taking subsets of the points. In this paper, we propose an efficient algorithm to compute such measures and we compare our approach with previously proposed methods bo…
Quasi-Monte Carlo point sets with small $t$-values and WAFOM
$(t, m, s)$ -网的$t$ -值是拟蒙特卡罗方法积分点集的重要判据，许多点集都是根据$t$ -值构造的，因为这导致小的积分误差界。最近，Matsumoto，Saito和Matoba提出了Walsh品质因数（WAFOM）作为一个快速计算的点集标准，确保高阶收敛的函数类非常高的光滑度。在本文中，我们考虑的点集的搜索算法，其$t$ -值和WAFOM都很小，以便有效地为更广泛的函数类。为此，我们用小的$t$ -值来固定数字$(t, m, s)$ -网（例如，Sobol或Niederreiter-Xing网），应用随机线性加扰，并根据WAFOM选择加扰的数字$(t, m, s)$网。实验结果表明，所得到的点集提高了光滑函数的收敛速度，并对非光滑函数具有鲁棒性。
We study approximate integration of a function $f$ over $[0,1]^s$ based on taking the median of $2r-1$ integral estimates derived from independently randomized $(t,m,s)$-nets in base $2$. The nets are randomized by Matousek's random linear scramble with a digital shift. If $f$ is analytic over $[0,1]^s$, then the probability that any one randomized net's estimate has an error larger than $2^{-cm^2/s}$ times a quantity depending on $f$ is $O(1/\sqrt{m})$ for any $c<3\log(2)/π^2\approx 0.21$. As a result the median of the distribution of these scrambled nets has an error that is $O(n^{-c\log(n)/…
QMC integration based on arbitrary (t,m,s)-nets yields optimal convergence rates on several scales of function spaces
Institut für Mathematik, Universität Osnabrück（奥斯纳布吕克大学）；School of Mathematics and Statistics, University of New South Wales（新南威尔士大学）；Stuttgart Technology Center, Sony Semiconductor Solutions Europe（索尼半导体解决方案欧洲斯图加特技术中心）；Mathematisches Institut, Friedrich-Schiller-Universität Jena（耶拿大学）
Quasi-Monte Carlo confidence intervals using quantiles of randomized nets
Johann Radon Institute for Computational and Applied Mathematics, ÖAW（约翰·拉多纳计算与应用数学研究所，奥地利科学院）；Institute of Fundamental and Transdisciplinary Research, Zhejiang University（浙江大学基础与交叉研究学院）
This article provides a strong law of large numbers for integration on digital nets randomized by a nested uniform scramble. The motivating problem is optimization over some variables of an integral over others, arising in Bayesian optimization. This strong law requires that the integrand have a finite moment of order $p$ for some $p>1$. Previously known results implied a strong law only for Riemann integrable functions. Previous general weak laws of large numbers for scrambled nets require a square integrable integrand. We generalize from $L^2$ to $L^p$ for $p>1$ via the Riesz-Thorin interpol…
We present an explicit construction of infinite sequences of points $(\boldsymbol{x}_0,\boldsymbol{x}_1, \boldsymbol{x}_2, \ldots)$ in the $d$-dimensional unit-cube whose periodic $L_2$-discrepancy satisfies $$L_{2,N}^{\rm per}(\{\boldsymbol{x}_0,\boldsymbol{x}_1,\ldots, \boldsymbol{x}_{N-1}\}) \le C_d N^{-1} (\log N)^{d/2} \quad \mbox{for all } N \ge 2,$$ where the factor $C_d > 0$ depends only on the dimension $d$. The construction is based on higher order digital sequences as introduced by J. Dick in the year 2008. The result is best possible in the order of magnitude in $N$ according to a …
Construction of interlaced scrambled polynomial lattice rules of arbitrary high order
Graduate School of Engineering, The University of Tokyo（东京大学工学研究科）；School of Mathematics and Statistics, The University of New South Wales（新南威尔士大学数学与统计学院）
The \emph{deterministic} sparse grid method, also known as Smolyak's method, is a well-established and widely used tool to tackle multivariate approximation problems, and there is a vast literature on it. Much less is known about \emph{randomized} versions of the sparse grid method. In this paper we analyze randomized sparse grid algorithms, namely randomized sparse grid quadratures for multivariate integration on the $D$-dimensional unit cube $[0,1)^D$. Let $d,s \in \mathbb{N}$ be such that $D=d\cdot s$. The $s$-dimensional building blocks of the sparse grid quadratures are based on stratifie…
We present LatNet Builder, a software tool to find good parameters for lattice rules, polynomial lattice rules, and digital nets in base 2, for quasi-Monte Carlo (QMC) and randomized quasi-Monte Carlo (RQMC) sampling over the $s$-dimensional unit hypercube. The selection criteria are figures of merit that give different weights to different subsets of coordinates. They are upper bounds on the worst-case error (for QMC) or variance (for RQMC) for integrands rescaled to have a norm of at most one in certain Hilbert spaces of functions. We summarize what are the various Hilbert spaces, discrepanc…
We construct a least squares approximation method for the recovery of complex-valued functions from a reproducing kernel Hilbert space on $D \subset \mathbb{R}^d$. The nodes are drawn at random for the whole class of functions and the error is measured in $L_2(D,\varrho_D)$. We prove worst-case recovery guarantees by explicitly controlling all the involved constants. This leads to new preasymptotic recovery bounds with high probability for the error of Hyperbolic Fourier Regression on multivariate data. In addition, we further investigate its counterpart Hyperbolic Wavelet Regression also base…
