带惩罚的有限元方法的收敛性

在用有限元方法解二阶椭圆型方程的边值问题时,首先将边值问题化为一个等价的变分问题,即泛函的极值问题。对于第二,第三边值问题,在相应的变分问题中边界条件被吸收到泛函的表示式中。因而在求泛函极值时不再对允许函数类附加边界条件的约束。在这种情况下边界条件就称为自然边界条件,相应的变分问题称为无约束变分问题。而对于第一边值问题即所谓狄氏问题则不然,相应的等价变分问题是带约束的变分问题。求泛函极值时的允许函数类必须满足强加的边界条件。例如我们考虑二维有界区域Ω上的方程     

微分方程某些反问题的稳定性的性态

如所周知,微分方程的中心任务是寻求定解问题的解,即、寻找满足特定辅助条件的微分方程的解。相对于这个问题,微分方程的反问题则是由微分方程解的某种泛函来确定方程的系数、右端或解的定义域。数学物理中的许多经典的反问题都归结为解上述问题。 然而在这类问题中,不论是已知的信息还是未知的结果一般都是用函数来表征。求     

求解有关空间利用的调度问题的拟物方法

可将有关空间利用的调度问题看作四维时空中的Packing问题。对于三维空间中的Packing问题已有拟物型的求解方法,将此种方法加以适当引伸后得出了求解有关调度问题的拟物方法。它能被具体化为关于空间利用调度问题的专家系统或计算机辅助设计软件系统。     

线性多变量调节器的设计

调节器问题是自动控制理论研究的重要问题之一。调节器的设计要求是:求一个反馈控制器使闭环系统稳定并且使输出趋向于零(控制理论中称为输出调节)。很多实际系统的控制问题归结为线性多变量调节器的设计问题。例如经典控制理论中的调节问题和跟踪问题都可化为调节器问题,在经济系统中也提出了一些线性多变量调节器的设计问题。     

传球问题的几种递归解法

转化是解决数学问题的基本方法．解题时，我们总是把待解决的问题。通过转化过程。归结到一类已经解决或者比较容易解决的问题，最终获得原问题之解答．转化目标一般是一个与原问题不同的问题。但也可以是规模更小的同一个问题。此即为递归法．     

一类非单调互补问题的Canonical对偶问题研究

对于一类具有广泛应用背景的非单调互补问题,我们构建了这类问题的Canonical对偶问题。其对偶问题可以写成和原问题类似的互补问题。我们给出了对偶问题和原问题解之间的对偶关系,并且将对偶问题转化成一个一维优化问题,这不但可以方便的求解这类问题,也为研究这类问题性质提供了一个非常直观的研究工具。最后,本文给出了几个算例来演示对偶问题的性质。     

一阶复的超定偏微分方程组的拟Riemann-Hilbert问题

本文考虑一阶非线性超定组的拟Riemann-Hilbert问题,首先考虑全纯函数的拟R-H问题,得到解的表达式,然后考虑线性问题的拟R-H问题,把问题化为标准化形式,指出一部分指标对讨论拟R-H问题已失去作用,最后讨论非线性问题,利用Schwartz公式及不动点定理,证明了在一定条件下问题是可解的。     

算法的发现（Ⅱ）──对称差（的）分解法及其应用

本文摹仿古典数学的导数、差分概念，在组合优化中建立枚举章法下的一个方法一一对弥差分解法，给出一个求解某些问题的一般模式。用它统一地讨论组合最优化的六个基本图论问题：最短路问题，最小生成树问题，匹配问题，巡迥商问题，中国邮路问题和最大流问题。讨论表明，Ｂｅｌｌｍａｎ最优性原理，交错链，增值路等概念都是对称差分解法在具体问题中的自然结果。还表明，涉及上述大个问题的２０多个著名定理都是定理４的具体推论。     

辅助问题的构思和设计

数学问题的探讨求解是一个不断地把原问题向着简单的、熟悉的、或者是已经解决了的问题转化的过程。G·波利亚指出:“当原问题看来不可解时,人类的高明之处就在于会迂回绕过不能直接克服的障碍,就在于能想出某个适当的辅助问题”。这里说的辅助问题,也就是原问题转化过程中的桥梁:或者是从问题本身的结构层次上考虑,可以是原问题的一个简单的、具有启发和引导作用的特殊情形;或者是从这个问题与另一个问题之间的对应关系上考虑,可以是建立在另一个系统上的与原问题邻近     

考虑货架存放期的经济批量排产问题的求解

在化工、造纸、制药、钢铁等工业生产中，一台设备或一条生产线可以生产多种产品的情况很常见。在生产中，如何安排各类产品的生产顺序以及生产数量显得十分重要。这类问题通常称作经济批量排产问题，这类问题是生产库存中的经典问题。本文研究的经济批量排产问题考虑了产品货架存放期因素，针对以往研究的不足，本文提出用批量变动方法求解该类问题，由计算结果显示，按照这种排产方法花费的成本要低于其他两种经济批量排产问题常用的方法。     

区间概率信息条件下的风险型决策问题的解法探讨

区间概率信息条件下的决策问题是介于不确定型决策与风险型决策之间的一类特殊的决策问题。基于区间概率的定义及其数学特征,利用最大熵准则将区间概率转化为点概率,从而实现了区间概率信息条件决策问题的求解。     

Rare Event Probability Estimation in the Presence of Epistemic Uncertainty on Input Probability Distribution Parameters

The accurate estimation of rare event probabilities is a crucial problem in engineering to characterize the reliability of complex systems. Several methods such as Importance Sampling or Importance Splitting have been proposed to perform the estimation of such events more accurately (i.e., with a lower variance) than crude Monte Carlo method. However, these methods assume that the probability distributions of the input variables are exactly defined (e.g., mean and covariance matrix perfectly known if the input variables are defined through Gaussian laws) and are not able to determine the impact of a change in the input distribution parameters on the probability of interest. The problem considered in this paper is the propagation of the input distribution parameter uncertainty defined by intervals to the rare event probability. This problem induces intricate optimization and numerous probability estimations in order to determine the upper and lower bounds of the probability estimate. The calculation of these bounds is often numerically intractable for rare event probability (say 10^?5), due to the high computational cost required. A new methodology is proposed to solve this problem with a reduced simulation budget, using the adaptive Importance Sampling. To this end, a method for estimating the Importance Sampling optimal auxiliary distribution is proposed, based on preceding Importance Sampling estimations. Furthermore, a Kriging-based adaptive Importance Sampling is used in order to minimize the number of evaluations of the computationally expensive simulation code. To determine the bounds of the probability estimate, an evolutionary algorithm is employed. This algorithm has been selected to deal with noisy problems since the Importance Sampling probability estimate is a random variable. The efficiency of the proposed approach, in terms of accuracy of the found results and computational cost, is assessed on academic and engineering test cases.     

Maximizing the probability of attaining a target prior to extinction

We present a dynamic programming-based solution to the problem of maximizing the probability of attaining a target set before hitting a cemetery set for a discrete-time Markov control process. Under mild hypotheses we establish that there exists a deterministic stationary policy that achieves the maximum value of this probability. We demonstrate how the maximization of this probability can be computed through the maximization of an expected total reward until the first hitting time to either the target or the cemetery set. Martingale characterizations of thrifty, equalizing, and optimal policies in the context of our problem are also established.     

带利率的更新风险模型的破产概率的上界估计

本研究了在常利率条件下普通更新风险模型的破产概率问题．采用一种递推的方法给出了这种情况下破产概率的一个上界估计．     

求无穷级数和以及多重积分极限的概率方法

文 [1 ]举例说明了概率思想在求无穷级数的和以及多重积分极限方面的应用 ,本文将对文 [1 ]中的例 1加以推广 ,拓展这一类题的解题思路 ,并对例 2用不同的概率方法加以证明 ,从而使数学分析与概率统计的有关知识联系起来 ,达到知识的融汇贯通 .     

On Non-Uniqueness of Probability Solutions to the Two-Dimensional Stationary Fokker–Planck–Kolmogorov Equation

The problem of uniqueness of probability solutions to the two-dimensional stationary Fokker–Planck–Kolmogorov equation is considered. Under broad conditions, it is proved that the existence of two different probability solutions implies the existence of an infinite set of linearly independent probability solutions.     

An anytime deduction algorithm for the probabilistic logic and entailment problems

We study two basic problems of probabilistic reasoning: the probabilistic logic and the probabilistic entailment problems. The first one can be defined as follows. Given a set of logical sentences and probabilities that these sentences are true, the aim is to determine whether these probabilities are consistent or not. Given a consistent set of logical sentences and probabilities, the probabilistic entailment problem consists in determining the range of the possible values of the probability associated with additional sentences while maintaining a consistent set of sentences and probabilities.This paper proposes a general approach based on an anytime deduction method that allows the follow-up of the reasoning when checking consistency for the probabilistic logic problem or when determining the probability intervals for the probabilistic entailment problem. Considering a series of subsets of sentences and probabilities, the approach proceeds by computing increasingly narrow probability intervals that either show a contradiction or that contain the tightest entailed probability interval. Computational experience have been conducted to compare the proposed anytime deduction method, called ad-psat with an exact one, psatcol, using column generation techniques, both with respect to the range of the probability intervals and the computing times.     

Adaptive Stabilization of Nonlinear Stochastic Systems

The purpose of this paper is to study the problem of asymptotic stabilization in probability of nonlinear stochastic differential systems with unknown parameters. With this aim, we introduce the concept of an adaptive control Lyapunov function for stochastic systems and we use the stochastic version of Artstein's theorem to design an adaptive stabilizer. In this framework the problem of adaptive stabilization of a nonlinear stochastic system is reduced to the problem of asymptotic stabilization in probability of a modified system. The design of an adaptive control Lyapunov function is illustrated by the example of adaptively quadratically stabilizable in probability stochastic differential systems. Accepted 9 December 1996