积分型变测度算法及其若干应用

本文对积分型变测度算法作进一步分析,并考虑该算法在区域变动算法,变测度算法的重心序列,无限维测度的有限维逼近等问题上的应用。     

有约束的变测度积分——水平集算法

提出了一种有约束的变测度积分-水平集的算法,对不同的箱子采用不同的测度,结合确定性数论方法选取一致分布佳点集来代替Monte-Carlo随机投点,使水平值充分地下降,更快地到达全局最小,从而提高算法的计算效率.给出了算法的收敛性证明,并通过数值算例验证了它的有效性.     

无限维空间中总极值的有限维逼近

变分学、最优控制、微分对策等问题,要求考虑无限维空间中的总极值问题,但实际计算中只能得出有限维空间中的解。本文利用积分型总极值途径和变测度的思想,给出了最优性条件,算法及从有限维过渡到无限维的收敛性,最后还给出构造变测度序列的两个例子。     

积分型求总极值的变测度算法

本文考虑求总极值的变测度方法,先引进了Q测度序列的Q收敛以及依赖于测度的均值,方差和高阶矩等概念。利用这些概念,得到了总极值的最优性条件。而后再给出变测度算法,并证明了算法的收敛性。     

实变函数反例研究(Ⅱ)

列举了实变函数中的多个反例.这些反例说明,在条件改变之后一些重要结论不再成立.这可增强人们对实变函数内容的深刻认识.文中的测度与积分都不是Lebesgue测度与积分.     

实变函数反例研究(Ⅰ)

在一般测度积分(非Lebesgue测度与积分)的框架下构造了实变函数中的多个反例.它们说明不同概念之间的区别,以及一些常用结论在缺乏相应的条件之后不再成立.这有力地补充了教材[1].     

谈谈Lebesgue测度概念的建立

我们知道,测度是R~n中点集E的一种量度,记为mE,它是长度,面积,体积概念的推广,测度概念与积分概念紧密相关,测度论不仅是积分理论的基础,而且在近代分析、概率论等致学分支和其它一些科学领域中也有着广泛的应用。 在实变函数论中,建立Lebesgue测度(筒称L测度)概念与Lebesgue积分(简称L积     

基于模糊积分的金融数据建模方法

以非可加模糊测度代替经典可加测度,基于模糊积分建立非线性回归模型是新近出现的数据建模方法.该方法充分考虑自变量因素之间的信息熔合(含协同或冲突)作用.本文完整地给出了适用于实数范围内的基于模糊积分(含Choquet积分和(S)ipo(s)积分)的多元非线性回归模型转化为普通线性回归模型的非线性转换方法及其简化算法.并将该方法应用于金融市场数据分析,结果表明效果较之普通多元线性回归有大的提高,且方法简便容易应用.     

推广的Cauchy定理的初等证明

本文给出一个推广的 Cauchy 积分定理的初等证明.它不涉及 Lebesgue 测度和积分等实变函数理论,直观易懂.     

修正积分水平集方法的随机实现算法

本文提出了一个从随机到确定性的变测度算法，通过对不同的箱子采用不同的测度，将Monte-Carlo随机投点与确定性数论方法相结合的策略，使水平值充分地下降．最后，给出了实现算法收敛性并通过数值实验验证了其有效性．     

The method of mechanical quadratures for integral equations with fixed singularity

We investigate the method of mechanical quadratures for integral equations with fixed singularity. We establish estimates of the error of this method based on a quadrature process, which is the best in the class of differentiable functions. We prove the convergence of the method in finite-dimensional and uniform metrics. We find that the investigated quadrature method is optimal by order on the Hölder class of functions.     

On the limited memory BFGS method for large scale optimization

We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. We compare its performance with that of the method developed by Buckley and LeNir (1985), which combines cycles of BFGS steps and conjugate direction steps. Our numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence. We show that the L-BFGS method can be greatly accelerated by means of a simple scaling. We then compare the L-BFGS method with the partitioned quasi-Newton method of Griewank and Toint (1982a). The results show that, for some problems, the partitioned quasi-Newton method is clearly superior to the L-BFGS method. However we find that for other problems the L-BFGS method is very competitive due to its low iteration cost. We also study the convergence properties of the L-BFGS method, and prove global convergence on uniformly convex problems.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under contract DE-FG02-87ER25047, and by National Science Foundation Grant No. DCR-86-02071.     

A domain decomposition method based on the iterative operator splitting method

In this article a new approach is proposed for constructing a domain decomposition method based on the iterative operator splitting method. The convergence properties of such a method are studied. The main feature of the proposed idea is the decoupling of space and time. We present a multi-iterative operator splitting method that combines iteratively the space and time splitting. We confirm with numerical applications the effectiveness of the proposed iterative operator splitting method in comparison with the classical Schwarz waveform relaxation method as a standard method for domain decomposition. We provide improved results and convergence rates.     

Generalizing the modeling of fuzzy logic controllers by parameterized aggregation operators

We describe the type of reasoning used in the typical fuzzy logic controller, the Mamdani reasoning method. We point out the basic assumptions in this model. We discuss the S-OWA operators which provide families of parameterized “andlike” and “orlike” operators. We generalize the Mamdani model by introducing these operators. We introduce a method, which we call Direct Fuzzy Reasoning (DFR), which results from one choice of the parameters. We develop some learning algorithms for the new method. We show how the Takagi-Sugeno-Kang (TSK) method of reasoning is an example of this DFR method.     

On a numerical solution of the Cauchy problem for the Laplace equation by the fundamental solutions method

We discuss a numerical method to solve a Cauchy problem for the Laplace equation in the two-dimensional annular domain. We consider the case that the Cauchy data is given on an arc. We develop an approximation method based of the fundamental solutions method using the least squares method with Tikhonov regularization. The effectiveness of our method is examined by a numerical experiment. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Finite number of iterations in the two-step extragradient method

We consider two-step extragradient method. This method is used for solving variational inequalities and related problems. We show the convergence of the method in a finite number of iterations when the condition of sharpness is fulfilled.     

Three methods for two‐sided bounds of eigenvalues—A comparison

We compare three finite element‐based methods designed for two‐sided bounds of eigenvalues of symmetric elliptic second order operators. The first method is known as the Lehmann–Goerisch method. The second method is based on Crouzeix–Raviart nonconforming finite element method. The third one is a combination of generalized Weinstein and Kato bounds with complementarity‐based estimators. We concisely describe these methods and use them to solve three numerical examples. We compare their accuracy, computational performance, and generality in both the lowest and higher order case.     

Optimally accurate Petrov-Galerkin method of finite elements

We perform analysis for a finite elements method applied to the singular self-adjoint problem. This method uses continuous piecewise polynomial spaces for the trial and the test spaces. We fit the trial polynomial space by piecewise exponentials and we apply so exponentially fitted Galerkin method to singular self-adjoint problem by approximating driving terms by Lagrange piecewise polynomials, linear, quadratic and cubic. We measure the erroe in max norm. We show that method is optimal of the first order in the error estimate. We also give numerical results for the Galerkin approximation.     

On possibility of obtaining linear accuracy evaluation of approximate solutions to inverse problems

We consider a concept of linear a priori estimate of the accuracy for approximate solutions to inverse problems with perturbed data. We establish that if the linear estimate is valid for a method of solving the inverse problem, then the inverse problem is well-posed according to Tikhonov. We also find conditions, which ensure the converse for the method of solving the inverse problem independent on the error levels of data. This method is well-known method of quasi-solutions by V. K. Ivanov. It provides for well-posed (according to Tikhonov) inverse problems the existence of linear estimates. If the error levels of data are known, a method of solving well-posed according to Tikhonov inverse problems is proposed. This method called the residual method on the correctness set (RMCS) ensures linear estimates for approximate solutions. We give an algorithm for finding linear estimates in the RMCS.     

A modified walk-on-sphere method for high dimensional fractional Poisson equation

We develop walk-on-sphere method for fractional Poisson equations with Dirichilet boundary conditions in high dimensions. The walk-on-sphere method is based on probabilistic representation of the fractional Poisson equation. We propose efficient quadrature rules to evaluate integral representation in the ball and apply rejection sampling method to drawing from the computed probabilities in general domains. Moreover, we provide an estimate of the number of walks in the mean value for the method when the domain is a ball. We show that the number of walks is increasing in the fractional order and the distance of the starting point to the origin. We also give the relationship between the Green function of fractional Laplace equation and that of the classical Laplace equation. Numerical results for problems in 2–10 dimensions verify our theory and the efficiency of the modified walk-on-sphere method.