随机化伪蒙特卡罗方法——攀登的（t,m,s）—网与（t,s）—序列

通过随机地置换ｂ进（ｔ,ｍ,ｓ）－网或（ｔ,ｓ）－序列的ｂ进数字,Ｏｗｅｎ（１９９５）提出了一种随机化蒙特卡罗积分法．这种方法是伪蒙特卡罗方法与蒙特卡罗方法的杂成,它体现了这两种技术的优点．本文介绍这一方法的有关背景及近年来的研究进展情况．     

蒙特卡罗模拟法在边坡可靠性分析中的运用

江永红．蒙特卡罗模拟法在边坡可靠性分析中的运用．数理统计与管理，１９９８，１７（１），１３～１６．可靠性分析是边坡工程及滑坡治理中的重要研究课题。鉴于决定边坡可靠性的诸变量多为随机变量，本文论述了用蒙特卡罗模拟法计算边坡可靠度的基本原理，对模拟次数确定、计算误差估计等问题提出了解决办法，并结合具体运用说明该方法的实施步骤     

关于伪欧氏空间

本文提出了一些新概念，ｐ—伪转置阵，ｐ—伪正交阵，ｐ—伪对称阵，从而能以这些矩阵为工具研究伪欧氏空间的性质以及空间中两个特殊线性变换，伪正交变换和伪对称变换．本文得到的主要结果是定理２，定理３，定理４及定理６．本文还指出了北大编《高等代数》第二版中的伪正交变换习题的一个错误．     

从不定度量空间形式到不定度量空间形式的等距浸入

设Msm(c)是等距浸入在2m-1维不定度量空间形式Ns2m-1((?))(c<(?))中的m维不定度量空间形式．若Msm(c)是极小的,我们证明Msm必定是有同一个指标s的2m-1维伪球面中的平坦子流形．我们还用孤立子理论给出了Ns2m-1中平坦的指标为s的子流形与系统之间的对应．     

解抛物型方程的半离散精细积分法

本讨论抛物型方程混合问题的解法．提出在有限元半离散过程后，用精细积分法获得一个较好的解，并且分析了这种方法的误差，证明了用这种方法和二次插值，在节点上有O(h^4)的超收敛性．     

关于Φ-伪压缩型映象的具误差的Ishikawa和Mann迭代过程的收敛性问题

往Ｂａｎａｃｈ空间中．研究了多值Φ－伪压缩型映象的具误差的Ｉｓｈｉｋａｗａ和Ｍａｎｎ迭代过程的收敛 性问题，所得结果改进、发展和统一了许多人的最新结果．     

三值R0命题逻辑系统的随机化

利用赋值集的随机化方法,在三值R0命题逻辑系统中提出了公式的随机真度和随机距离,建立了随机逻辑度量空间.指出当取均匀概率测度,且各概率测度均为1/3时,随机真度就转化为计量逻辑学中的真度,同时两公式间的随机距离就转化为计量逻辑学中的伪距离,从而建立了更具一般性的随机逻辑度量空间.     

Banach空间中严格渐近伪压缩映象的收敛性问题

在一致凸的Banach空间中，采用新的证明方法研究了严格渐近伪压缩映象和渐近非膨胀映象带误差的修正的Mann和Ishikawa迭代程序的收敛性问题，不要求定义域、值域有界，且迭代系数更简单．     

SL(2,C)中的伪非初等子群和伪Kleinian群

定义了SL(2,C)中的伪非初等群和伪Kleinian群，得到了它们的离散准则和收敛定理．     

n值G?del命题逻辑系统中公式的条件随机真度

基于条件概率的思想,利用赋值集的随机化方法,在n值命题G(o|¨)del逻辑系统中引入公式的条件随机真度,证明了条件随机真度的MP规则和HS规则.引入公式间的条件随机相似度和条件伪距离,建立了条件随机逻辑度量空间,证明了条件随机逻辑度量空间中二元运算的连续性.     

Strong tractability of integration using scrambled Niederreiter points

We study the randomized worst-case error and the randomized error of scrambled quasi-Monte Carlo (QMC) quadrature as proposed by Owen. The function spaces considered in this article are the weighted Hilbert spaces generated by Haar-like wavelets and the weighted Sobolev-Hilbert spaces. Conditions are found under which multivariate integration is strongly tractable in the randomized worst-case setting and the randomized setting, respectively. The -exponents of strong tractability are found for the scrambled Niederreiter nets and sequences. The sufficient conditions for strong tractability for Sobolev spaces are more lenient for scrambled QMC quadratures than those for deterministic QMC net quadratures.

     

TRACTABILITY OF MULTIVARIATE INTEGRATION PROBLEM FOR PERIODIC CONTINUOUS FUNCTIONS

The authors study the tractability and strong tractability of a multivariate integration problem in the worst case setting for weighted l-periodic continuous functions spaces of d coordinates with absolutely convergent Fourier series.The authors reduce the initial error by a factor ε for functions from the unit ball of the weighted periodic contin- uous functions spaces.Tractability is the minimal number of function samples required to solve the problem in polynomial in ε~(-1)and d.and the strong tractability is the pres- ence of only a polynomial dependence in ε.This problem has been recently studied for quasi-Monte Carlo quadrature rules.quadrature rules with non-negative coefficients. and rules for which all quadrature weights are arbitrary for weighted Korobov spaces of smooth periodic functions of d variables.The authors show that the tractability and strong tractability of a multivariate integration problem in worst case setting hold for the weighted periodic continuous functions spaces with absolutely convergent Fourier series under the same assumptions as in Ref.[14]on the weights of the Korobov space for quasi-Monte Carlo rules and rules for which all quadrature weights are non-negative.The arguments are not constructive.     

A central limit theorem and improved error bounds for a hybrid-Monte Carlo sequence with applications in computational finance

In problems of moderate dimensions, the quasi-Monte Carlo method usually provides better estimates than the Monte Carlo method. However, as the dimension of the problem increases, the advantages of the quasi-Monte Carlo method diminish quickly. A remedy for this problem is to use hybrid sequences; sequences that combine pseudorandom and low-discrepancy vectors. In this paper we discuss a particular hybrid sequence called the mixed sequence. We will provide improved discrepancy bounds for this sequence and prove a central limit theorem for the corresponding estimator. We will also provide numerical results that compare the mixed sequence with the Monte Carlo and randomized quasi-Monte Carlo methods.     

Error bounds for quasi-Monte Carlo integration with uniform point sets

We establish new error bounds for quasi-Monte Carlo integration for node sets with a special kind of uniformity property. The methods of proving these error bounds work for arbitrary probability spaces. Only the bounds in terms of the modulus of continuity of the integrand require also the structure of a metric space.     

Intractability Results for Positive Quadrature Formulas and Extremal Problems for Trigonometric Polynomials

Lower bounds for the error of quadrature formulas with positive weights are proved. We get intractability results for quasi-Monte Carlo methods and, more generally, for positive formulas. We consider general classes of functions but concentrate on lower bounds for relatively small classes of trigonometric polynomials. We also conjecture that similar lower bounds hold for arbitrary quadrature formulas and state different equivalent conjectures concerning positive definiteness of certain matrices and certain extremal problems for trigonometric polynomials. We also study classes of functions with weighted norms where some variables are “more important” than others. Positive quadrature formulas are then tractable iff the sum of the weights is bounded.     

Minimal relative entropy for equivalent martingale measures by low-discrepancy sequence in Lévy process

In this paper, we focused on computing the minimal relative entropy between the original probability and all of the equivalent martin gale measure for the Lévy process. For this purpose, the quasiMonte Carlo method is used. The probability with minimal relative entropy has many suitable properties. This probability has the minimal Kullback-Leibler distance to the original probability. Also, by using the minimal relative entropy the exponential utility indifference price can be found. In this paper, the Monte Carlo and quasi-Monte Carlo methods have been applied. In the quasi-Monte Carlo method, two types of widely used lowdiscrepancy sequences, Halton sequence and Sobol sequence, are used. These methods have been used for exponential Lévy process such as variance gamma and CGMY process. In these two processes, the minimal relative entropy has been computed by Monte Carlo and quasi-Monte Carlo, and compared their results. The results show that quasi-Monte Carlo with Sobol sequence performs better in terms of fast convergence and less error. Finally, this method by fitting the variance gamma model and parameters estimation for the model has been implemented for financial data and the corresponding minimal relative entropy has been computed.     

Efficient deterministic numerical simulation of stochastic asset-liability management models in life insurance

New regulations, stronger competitions and more volatile capital markets have increased the demand for stochastic asset-liability management (ALM) models for insurance companies in recent years. The numerical simulation of such models is usually performed by Monte Carlo methods which suffer from a slow and erratic convergence, though. As alternatives to Monte Carlo simulation, we propose and investigate in this article the use of deterministic integration schemes, such as quasi-Monte Carlo and sparse grid quadrature methods. Numerical experiments with different ALM models for portfolios of participating life insurance products demonstrate that these deterministic methods often converge faster, are less erratic and produce more accurate results than Monte Carlo simulation even for small sample sizes and complex models if the methods are combined with adaptivity and dimension reduction techniques. In addition, we show by an analysis of variance (ANOVA) that ALM problems are often of very low effective dimension which provides a theoretical explanation for the success of the deterministic quadrature methods.     

Generalized von Neumann–Kakutani transformation and random-start scrambled Halton sequences

It is a well-known fact that the Halton sequence exhibits poor uniformity in high dimensions. Starting with Braaten and Weller in 1979, several researchers introduced permutations to scramble the digits of the van der Corput sequences that make up the Halton sequence, in order to improve the uniformity of the Halton sequence. These sequences are called scrambled Halton, or generalized Halton sequences. Another significant result on the Halton sequence was the fact that it could be represented as the orbit of the von Neumann–Kakutani transformation, as observed by Lambert in 1982. In this paper, I will show that a scrambled Halton sequence can be represented as the orbit of an appropriately generalized von Neumann–Kakutani transformation. A practical implication of this result is that it gives a new family of randomized quasi-Monte Carlo sequences: random-start scrambled Halton sequences. This work generalizes random-start Halton sequences of Wang and Hickernell. Numerical results show that random-start scrambled Halton sequences can improve on the sample variance of random-start Halton sequences by factors as high as 7000.     

On numerical integration by the shift and application to Wiener space

Since the advantages of quasi-Monte Carlo methods vanish when the dimension of the basic space increases, the question arises whether there are better methods than the classical Monte Carlo in large or infinite-dimensional basic spaces. We study here the use of the shift operator with the pointwise ergodic theorem whose implementation is particularly interesting. After recalling the theoretical results on the speed of convergence in a form useful for applications, we give sufficient criteria for the law of iterated logarithm in several cases and, in particular, in situations involving the Wiener space.     

Digital nets with infinite digit expansions and construction of folded digital nets for quasi-Monte Carlo integration

In this paper we study quasi-Monte Carlo integration of smooth functions using digital nets. We fold digital nets over ${\mathbb{Z}}_b$ by means of the $b$-adic tent transformation, which has recently been introduced by the authors, and employ such folded digital nets as quadrature points. We first analyze the worst-case error of quasi-Monte Carlo rules using folded digital nets in reproducing kernel Hilbert spaces. Here we need to permit digital nets with “infinite digit expansions”, which are beyond the scope of the classical definition of digital nets. We overcome this issue by considering the infinite product of cyclic groups and the characters on it. We then give an explicit means of constructing good folded digital nets as follows: we use higher order polynomial lattice point sets for digital nets and show that the component-by-component construction can find good folded higher order polynomial lattice rules that achieve the optimal convergence rate of the worst-case error in certain Sobolev spaces of smoothness of arbitrarily high order.