关于ρ(L_(r,w))、ρ(J_w)的最小值

本文证明了当线性方程组系数矩阵 A之 Jacobi迭代矩阵 B=L+ U≥ 0 ,ρ( B) <1时 Gauss-Seidel法之迭代矩阵 G=L1,1的谱半径 ρ( G) =ρ( L1,1)是 ρ( Lr,w) ( 0≤ r≤w≤ 1 ,w>0 )中的最小值 ,即此时 Gauss-Seidel迭代是 AOR法中收敛最快的迭代法 .并且对 JOR法 (谱半径为 ρ( Jw) )和 SAOR法也作了相应的论述 .     

输运方程特征值问题的高精度求积方法

<正>1引言考虑平板各向异性散射和裂变的输运方程:其中,2a为平板厚度.-a≤x≤a,-1≤μ,μ′≤1,V是临界特征值.如何求解输运方程的最大的简单特征值问题是一个重要课题[1].关于它的存在性已     

侧向加热腔体中的多圈型对流斑图

基于流体力学方程组的数值模拟,研究了倾角θ=90°时侧向加热的大高宽比腔体中的对流斑图.对于Prandtl数Pr=6.99的流体,在相对Rayleigh数2≤Ra r≤25的范围内,腔体中发生的是单圈型对流斑图.对于Pr=0.0272的流体,取Ra r=13.9,随着计算时间的发展,腔体中由最初的单圈型对流斑图过渡到多圈型对流斑图,这是出现在侧向加热大高宽比腔体中的新型对流斑图.对不同Ra r情况的计算结果表明,Ra r对对流斑图的形成存在明显的影响.当Ra r≤4.4时是单圈型对流滚动;当Ra r=8.9~11.1时是过渡状态;当Ra r≥13.9时是多圈型对流滚动.对流最大振幅和Nusselt数Nu随着相对Rayleigh数的增加而增加.该对流斑图与Pr=6.99时对流斑图的比较说明,对流斑图的形成依赖于Prandtl数.     

交换偏序半群的偏序扩张

设(S,·,≤)为偏序可换半群,本文给出将S的偏序≤扩张为满足一定条件的偏序≤^{*的充要条件.特别地,如果(S,·,≤)为可消偏序可换幺半群,本文给出将S的偏序≤扩张为可消偏序≤*且S的每个元素在≤*下均在正锥中的充要条件.本文还给出将偏序可换幺半群S的偏序≤扩张为≤*且使得S的有限元素子集在≤*下是一条链的充要条件.}     

两边夹的思想方法在高中数学中的应用

若an≤bn≤cn,且lim an=limcn=A,则 n→∞ n→∞limbn=A,这是高等数学中的两边夹定理.与n→∞之相仿,初等数学中也有一个结构相似的两边夹结论:若A≤a≤A,则a=A.这虽是一个显而易见的结论,但在高中数学中却有不少应用.     

关于单机两个客户竞争排序问题1||∑wAjcAj:fBmax≤Q的一个注记

研究了单机两个客户竞争排序问题1||∑wAjcAj:fBmax≤Q,证明了该问题与问题1|MAi|∑wjcj及问题1|hi,pmtn|∑wjcj之间是相互等价的.对wj=pj时的特殊情形,指出了问题1||∑wAjcAj:fBmax≤Q存在近似比为2的最长处理时间优先算法(LPT)且该界是紧的,对wj任意的一般情形,指出了问题1||∑wAjcAj:fBmax≤Q存在近似比为4+ε的近似算法.当客户B的工件数是常数时,对问题1||∑wAjcAj:fBmax≤Q则给出了伪多项式时间的动态规划算法.此外,指出了问题1||∑wAjcAj:∑wBjcBj ≤ Q具有多项式时间近似方案(PTAS).     

关于AOR迭代法的研究

本文论证了严格对角占优矩阵之AOR法的误差估计式中的误差估计常数hγ,ω（0≤γ≤ω0）的最小值是h1,1.     

具有易损坏储备部件可修系统谱的特性

研究了R(r;A+E)的性质,给出了修复系统预解式的特性.对任意给定的δ>0,r=a+bi,-μ+δ相似文献   

随机环境下M/M/c重试丢弃的ATM网络排队性能分析

在ATM网络中顾客的到达率和服务率都随着环境的变化而变化.本文考虑的是具有随机环境的多服务台排队模型,在随机状态为i(1≤i≤m)时,到达时间间隔和服务时间分布分别是服从参数为λ_i和μ_1的指数分布,系统具有有限缓冲位置和无限位置的重试轨道,重试失败的顾客以一定概率被系统丢弃而永远离开系统.运用拟生灭过程方法,我们求得了稳态条件及在稳态下各个环境上各项条件排队指标及平均排队指标,通过数值模拟说明了高峰期到达率和其它参数对系统状态及忙期循环的影响.     

扩展de Bruijn图的生成树和广播

扩展de Bruijn图EB(d,m;h1,h2,…,hk)是de Bruijn图的一种推广,它是一种再要的网络互连结构.本文主要研究扩展de Bruijn图中的有根生成树,证明了对任何顶点u和任意整数r:2≤r≤d,扩展de Bruijn图都有以u为根且深度为[log(?),d]·max{hi:1≤i≤k}的rk-叉生成树,并由此获得了扩展de Bruijn图的广播时间的上界.     

An Indefinite Stochastic Linear Quadratic Optimal Control Problem with Delay and Related Forward–Backward Stochastic Differential Equations

In this paper, we will study an indefinite stochastic linear quadratic optimal control problem, where the controlled system is described by a stochastic differential equation with delay. By introducing the relaxed compensator as a novel method, we obtain the well-posedness of this linear quadratic problem for indefinite case. And then, we discuss the uniqueness and existence of the solutions for a kind of anticipated forward–backward stochastic differential delayed equations. Based on this, we derive the solvability of the corresponding stochastic Hamiltonian systems, and give the explicit representation of the optimal control for the linear quadratic problem with delay in an open-loop form. The theoretical results are validated as well on the control problems of engineering and economics under indefinite condition.     

Stability analysis and optimal control of stochastic singular systems

In this paper, problems of stability and optimal control for a class of stochastic singular systems are studied. Firstly, under some appropriate assumptions, some new results about mean-square admissibility are developed and the corresponding LMI sufficient condition is given. Secondly, finite-time horizon and infinite-time horizon linear quadratic (LQ) control problems for the stochastic singular system are investigated, in which the coefficients are allowed to be random in control input and quadratic criterion. Some results involving new stochastic generalized Riccati equation are discussed as well. Finally, the proposed LQ control model for stochastic singular systems provides an appropriate and effective framework to study the portfolio selection problem in light of the recent development on general stochastic LQ problems.     

Global adapted solution of one-dimensional backward stochastic Riccati equations,with application to the mean–variance hedging

Backward stochastic Riccati equations are motivated by the solution of general linear quadratic optimal stochastic control problems with random coefficients, and the solution has been open in the general case. One distinguishing difficult feature is that the drift contains a quadratic term of the second unknown variable. In this paper, we obtain the global existence and uniqueness result for a general one-dimensional backward stochastic Riccati equation. This solves the one-dimensional case of Bismut–Peng's problem which was initially proposed by Bismut (Lecture Notes in Math. 649 (1978) 180). We use an approximation technique by constructing a sequence of monotone drifts and then passing to the limit. We make full use of the special structure of the underlying Riccati equation. The singular case is also discussed. Finally, the above results are applied to solve the mean–variance hedging problem with general random market conditions.     

Pathwise Stability of Degenerate Stochastic Evolutions

For linear stochastic evolution equations with linear multiplicative noise, a new method is presented for estimating the pathwise Lyapunov exponent. The method consists of finding a suitable (quadratic) Lyapunov function by means of solving an operator inequality. One of the appealing features of this approach is the possibility to show stabilizing effects of degenerate noise. The results are illustrated by applying them to the examples of a stochastic partial differential equation and a stochastic differential equation with delay. In the case of a stochastic delay differential equation our results improve upon earlier results.     

Mean‐variance hedging with basis risk

Basis risk arises in a number of financial and insurance risk management problems when the hedging assets do not perfectly match the underlying asset in a hedging program. Notable examples in insurance include the hedging for longevity risks, weather index–based insurance products, variable annuities, etc. In the presence of basis risk, a perfect hedging is impossible, and in this paper, we adopt a mean‐variance criterion to strike a balance between the expected hedging error and its variability. Under a time‐dependent diffusion model setup, explicit optimal solutions are derived for the hedging target being either a European option or a forward contract. The solutions are obtained by a delicate application of the linear quadratic control theory, the method of backward stochastic differential equation, and Malliavin calculus. A numerical example is presented to illustrate our theoretical results and their interesting implications.     

Smooth Solutions to Optimal Investment Models with Stochastic Volatilities and Portfolio Constraints

Abstract. This paper deals with an extension of Merton's optimal investment problem to a multidimensional model with stochastic volatility and portfolio constraints. The classical dynamic programming approach leads to a characterization of the value function as a viscosity solution of the highly nonlinear associated Bellman equation. A logarithmic transformation expresses the value function in terms of the solution to a semilinear parabolic equation with quadratic growth on the derivative term. Using a stochastic control representation and some approximations, we prove the existence of a smooth solution to this semilinear equation. An optimal portfolio is shown to exist, and is expressed in terms of the classical solution to this semilinear equation. This reduction is useful for studying numerical schemes for both the value function and the optimal portfolio. We illustrate our results with several examples of stochastic volatility models popular in the financial literature.     

Stochastic Linear Quadratic Optimal Control Problems

This paper is concerned with the stochastic linear quadratic optimal control problem (LQ problem, for short) for which the coefficients are allowed to be random and the cost functional is allowed to have a negative weight on the square of the control variable. Some intrinsic relations among the LQ problem, the stochastic maximum principle, and the (linear) forward—backward stochastic differential equations are established. Some results involving Riccati equation are discussed as well. Accepted 15 May 2000. Online publication 1 December 2000     

BSDEs with stochastic Lipschitz condition and quadratic PDEs in Hilbert spaces

This paper is devoted to real valued backward stochastic differential equations (BSDEs for short) with generators which satisfy a stochastic Lipschitz condition involving BMO martingales. This framework arises naturally when looking at the BSDE satisfied by the gradient of the solution to a BSDE with quadratic growth in Z$Z$. We first prove an existence and uniqueness result from which we deduce the differentiability with respect to parameters of solutions to quadratic BSDEs. Finally, we apply these results to prove the existence and uniqueness of a mild solution to a parabolic partial differential equation in Hilbert space with nonlinearity having quadratic growth in the gradient of the solution.     

Smooth Solutions to Optimal Investment Models with Stochastic Volatilities and Portfolio Constraints

   Abstract. This paper deals with an extension of Merton's optimal investment problem to a multidimensional model with stochastic volatility and portfolio constraints. The classical dynamic programming approach leads to a characterization of the value function as a viscosity solution of the highly nonlinear associated Bellman equation. A logarithmic transformation expresses the value function in terms of the solution to a semilinear parabolic equation with quadratic growth on the derivative term. Using a stochastic control representation and some approximations, we prove the existence of a smooth solution to this semilinear equation. An optimal portfolio is shown to exist, and is expressed in terms of the classical solution to this semilinear equation. This reduction is useful for studying numerical schemes for both the value function and the optimal portfolio. We illustrate our results with several examples of stochastic volatility models popular in the financial literature.     

Stochastic maximum principle for mean-field forward-backward stochastic control system with terminal state constraints

In this paper,we consider an optimal control problem with state constraints,where the control system is described by a mean-field forward-backward stochastic differential equation(MFFBSDE,for short)and the admissible control is mean-field type.Making full use of the backward stochastic differential equation theory,we transform the original control system into an equivalent backward form,i.e.,the equations in the control system are all backward.In addition,Ekeland’s variational principle helps us deal with the state constraints so that we get a stochastic maximum principle which characterizes the necessary condition of the optimal control.We also study a stochastic linear quadratic control problem with state constraints.