Discrete Stochastic Arithmetic for Validating Results of Numerical Software

The Discrete Stochastic Arithmetic DSA is a probabilistic approach for round-off error propagation. After a brief review of the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method, which is the basis of DSA, the concept of the “informatical zero”, also called “computational zero”, is defined. The stochastic order relations of the DSA are presented. The DSA is the joint use of the synchronous implementation of the CESTAC method and the stochastic order relations. After having summarized the asynchronous implementation of the CESTAC method, which has been used in the Prosolver software, and which has been legitimately criticized, the synchronous implementation is presented. Then the CADNA (Control of Accuracy and Debugging for Numerical Application) library which implements the DSA arithmetic is presented. It is shown that this library is able to dynamically control the validity of the hypotheses which must hold so that results provided by CESTAC method are reliable. If the hypotheses do not hold then warnings are printed in a special file. The user is informed that numerical anomalies have been detected. Depending on these warnings the user may conclude either that the results obtained are not reliable and that they cannot be correctly computed with this computer, or he may try to debug his code. It is shown that the numerical examples that support the criticisms and which make the Prosolver software fail, do not jeopardize the CADNA library.     

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method

Journal of the Operational Research Society -     

Extrapolation of the Stochastic Theta Numerical Method for Stochastic Differential Equations

ABSTRACT

The stochastic theta method is a family of implicit Euler methods for approximating solutions to Itô stochastic differential equations. It is proved that the weak error for the stochastic theta numerical method is of the correct form to apply Richardson extrapolation. Several computational examples illustrate the improvement in accuracy of the approximations when applying extrapolation.     

浅水中污染物扩散的自适应有限元分析法

介绍浅水中污染物扩散分析中的有限元法.分析包括两个部分:1)流场速度、水面高度的计算;2)根据扩散模型计算污染物浓度场.联合使用了自适应网格技术以期提高解的精度,同时减少计算时间和计算机内存的消耗.通过几个有已知解的实例验证了有限元公式和计算机程序.最后,使用这种联合方法分析泰国Chao Phraya河附近海湾中的污染物扩散.     

Solution of the Equation for Oil Spills Spreading over the Sea Surface by the Characteristic Method

Computational Mathematics and Mathematical Physics - A model of oil spill spreading described by partial differential equations based on the balance of the forces acting on an axisymmetric slick is...     

时间分数阶扩散方程的数值解法

分数阶微分方程在许多应用科学上比整数阶微分方程更能准确地模拟自然现象.考虑时间分数阶扩散方程,将一阶的时间导数用分数阶导数α(0<α<1)替换,给出了一种计算有效的隐式差分格式,并证明了这个隐式差分格式是无条件稳定和无条件收敛的,最后用数值例子说明差分格式是有效的.     

Convergence of a Stochastic Method for the Modeling of Polymeric Fluids

We present a convergence analysis of a stochastic method for numerical modeling of complex fluids using Brownian configuration fields (BCF) for shear flows. The analysis takes into account the special structure of the stochastic partial differential equations for shear flows. We establish the optimal rate of convergence. We also analyze the nature of the error by providing its leading order asymptotics.     

Numerical Approximation of Stochastic Theta Method for Random Periodic Solution of Stochastic Differential Equations

Acta Mathematicae Applicatae Sinica, English Series - In this paper, we make use of stochastic theta method to study the existence of the numerical approximation of random periodic solution. We...     

线性随机微分方程的离散波形松弛方法的几乎必然收敛性

提出了随机微分方程的离散型波形松弛方法,证明了它是几乎必然收敛的.此外,通过数值实验验证了所得结果.     

一种基于随机Taylor展开式的随机微分方程数值解法

给出了一种基于随机Taylor展开式的随机微分方程数值格式,证明了它的均方稳定性。此外,还证明了这种数值格式的均值意义下的局部收敛阶为2,均方意义下的局部收敛阶为1,均方强收敛阶为1.数值实验表明本文的方法比Euler法和Milstein方法具有更好的逼近效果.     

Weak Convergence of a Numerical Method for a Stochastic Heat Equation

Weak convergence with respect to a space of twice continuously differentiable test functions is established for a discretisation of a heat equation with homogeneous Dirichlet boundary conditions in one dimension, forced by a space-time Brownian motion. The discretisation is based on finite differences in space and time, incorporating a spectral approximation in space to the Brownian motion.     

A Convolution Method for Numerical Solution of Backward Stochastic Differential Equations

We propose a new method for the numerical solution of backward stochastic differential equations (BSDEs) which finds its roots in Fourier analysis. The method consists of an Euler time discretization of the BSDE with certain conditional expectations expressed in terms of Fourier transforms and computed using the fast Fourier transform (FFT). The problem of error control is addressed and a local error analysis is provided. We consider the extension of the method to forward-backward stochastic differential equations (FBSDEs) and reflected FBSDEs. Numerical examples are considered from finance demonstrating the performance of the method.     

具有多级选择的离散网络平衡设计模型与算法

本文提出了一种新的离散网络平衡设计二层规划模型,模型同时考虑了新增路段及已有路段的扩容,而且允许不同等级的扩容选择.模型求解中,上层采用粒子群算法,而下层则采用本文作者提出的仿射尺度内点算法.数值计算结果显示,本文构建的算法能够快速有效地求解这类新的网络平衡设计二层规划模型.     

Stochastic Models for Oil Prices and the Pricing of Futures on Oil

Abstract

In this article, we investigate and compare the performance of various one-factor diffusion models in their ability to capture the behaviour of Brent crude oil prices. New proposed models, which have a three-quarters power in the diffusion term, are found to outperform all other popular models tested. Analytic solutions for futures prices under the new models are found and used to calibrate market prices. Results from the calibration show that one of the new three-quarters models with a mean-reverting property outperforms other popular models in fitting and forecasting futures prices.