由软件和硬件构成的串联可修计算机系统的适定性及渐近稳定性

讨论由软件和硬件构成的串联可修计算机系统的时间依赖解,运用C_0-半群理论及算子理论,证明系统的适定性和时间依赖非负解的存在唯一性.通过研究系统相应算子的谱特征,得到系统时间依赖解的渐近稳定性.     

含奇异线的广义KdV方程的行波解

研究了一个广义KdV方程的行波解,在行波变换下,该方程转化成含奇异线的平面系统,通过平衡点分析定性地得到不同参数条件下系统解的特性.特别的,由于相平面上的奇异线的存在,系统具有一些特殊结构的解,例如compactons、kink-compactons、anti-kink-compactons,给出了这些解的积分表达式,并且由椭圆函数积分求出了精确解.     

具有多重严重故障和非严重故障和修复功能的系统的可靠性分析

该文研究一个具有多重严重故障和非严重故障和修复功能的系统的可靠性问题. 在泛函分析理论的框架下,将系统方程组写成一个 Banach 空间中的抽象初值问题,利用算子半群方法,研究了该系统的适定性、稳态解的存在性以及稳定性.表明: 在系统模型的假定下,所研究的系统是适定的,存在非负动态解和稳态解, 特别在范数意义下动态解收敛到稳态解.从而由系统稳态解得到的系统指标是可靠的.     

一类带修理工休假可修系统解的存在唯一性及正则性分析

考虑一类修理工可多重延误休假的n部件串联可修复系统解的存在唯一性及正则性问题.通过将系统模型方程转化为一组算子积分方程,利用不动点理论讨论该系统局部解的存在唯一性问题,再由一致先验估计和连续延拓讨论系统整体解的存在唯一性问题,继而分析解的正则性问题.为解决复杂可修复系统解的存在唯一性及正则性提供了可行性方法,并且方法同样适用于排队论系统和其他类似系统.     

一类受迫Liénard系统的最终零解

寻找一类带有时间依赖强迫项的Liénard系统的最终零解,这是一种当t±∞时趋于0的特殊有界解.由于不是微扰的Hamilton系统,所以不能使用Melnikov方法来判断最终零解的存在性.研究了一个逼近原系统的周期受迫系统序列的周期解序列,并且证明这个周期解序列有一个收敛子列,其极限就是原受迫Liénard系统的最终零解.其中使用Schauder不动点定理解决了由非Hamilton造成的困难.     

具有热储备的可修复平行系统在由常规错误引起失效下的渐进稳定性

讨论了具有热储备和两个独立相同部件的平行系统在由常规错误引起失效下的渐进稳定性.首先,利用Banach空间的Volttera算子方程得到了非负动态解的存在唯一性;然后,利用强连续线性算子半群理论证明了系统正的动态解的存在唯一性,而由于初始值不在定义域内,故得到的是mild解.但在t＞0时系统古典解存在唯一,所以此时mild解即为古典解.最后,利用线性算子半群稳定性的结果,证明了该动态解在范数意义下收敛到稳态解,进而得到了系统的渐进稳定性.     

一类可修复计算机系统的定性分析

本文讨论由软件和硬件构成的一类可修复计算机系统的动态解.运用C0-半群理论及算子理论,证明该系统的适定性和非负动态解的存在唯—性.通过研究系统相应算子的谱特征,得到系统的渐近稳定性.     

分段线性非线性汽车悬架系统的分岔行为

建立了由主、副簧组成的分段线性非线性悬架系统动力学模型,应用奇异性理论研究了两个自由度汽车悬架系统共振解的分岔,得到系统的转迁集和40组分岔图,发现了非常复杂的局部分岔,分岔图全面展示了这一系统的分岔特性.由系统参数与该系统的拓扑分岔解之间的联系,分析并得到了不同参数下系统的运动特性,为实现悬架参数的优化控制提供了理论依据.     

带扩散项人口模型的能稳性

该文研究了一类带扩散项的人口模型平衡解的反馈能稳性,控制器由对应于平衡解的线性化系统的LQ问题所导出.     

一类二次系统定义的双参数三次代数曲线解族

本文给出一类由二次系统定义的双参数三次代数曲线解族，研究族中曲线解的轨线成为分界线环或其一部份的充要条件及相应系统的全局相图，从而揭示了由代数曲线解确定的二次系统的异宿环（有界或无界）及退化奇点分支出同宿环的某些现象．另外，本文的结果表明文［３］中关于二次系统的三次代数曲线同宿环的结论是不完备的．     

The exponential matrix function and linear differential equations

Monte Carlo optimization has been shown to be useful in solving multivariate optimization problems. In most Monte Carlo simulations, to find the optimal solution of an integer programming problem, the computer gets close to the true optimal solution, but does not find the exact optimal. This can be a problem at times but it can be overcome by multi‐stage Monte Carlo integer programs that find a preliminary nearly optimal solution and use this as a focal point and springboard for finding the true optimal.     

The Cross-Entropy Method for Combinatorial and Continuous Optimization

We present a new and fast method, called the cross-entropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on Kullback-Leibler cross-entropy, importance sampling, Markov chain and Boltzmann distribution. We use importance sampling as an important ingredient for adaptive adjustment of the temperature in the Boltzmann distribution and use Kullback-Leibler cross-entropy to find the optimal solution. In fact, we use the mode of a unimodal importance sampling distribution, like the mode of beta distribution, as an estimate of the optimal solution for continuous optimization and Markov chains approach for combinatorial optimization. In the later case we show almost surely convergence of our algorithm to the optimal solution. Supporting numerical results for both continuous and combinatorial optimization problems are given as well. Our empirical studies suggest that the cross-entropy method has polynomial in the size of the problem running time complexity.     

Active Constraint Set Invariancy Sensitivity Analysis in Linear Optimization

Active constraint set invariancy sensitivity analysis is concerned with finding the range of parameter variation so that the perturbed problem has still an optimal solution with the same support set that the given optimal solution of the unperturbed problem has. However, in an optimization problem with inequality constraints, active constraint set invariancy sensitivity analysis aims to find the range of parameter variation, where the active constraints in a given optimal solution remains invariant.For the sake of simplicity, we consider the primal problem in standard form and consequently its dual may have an optimal solution with some active constraints. In this paper, the following question is answered: “what is the range of the parameter, where for each parameter value in this range, a dual optimal solution exists with exactly the same set of positive slack variables as for the current dual optimal solution?”. The differences of the results between the linear and convex quadratic optimization problems are highlighted too.     

Value-Estimation Function Method for Constrained Global Optimization

A novel value-estimation function method for global optimization problems with inequality constraints is proposed in this paper. The value-estimation function formulation is an auxiliary unconstrained optimization problem with a univariate parameter that represents an estimated optimal value of the objective function of the original optimization problem. A solution is optimal to the original problem if and only if it is also optimal to the auxiliary unconstrained optimization with the parameter set at the optimal objective value of the original problem, which turns out to be the unique root of a basic value-estimation function. A logarithmic-exponential value-estimation function formulation is further developed to acquire computational tractability and efficiency. The optimal objective value of the original problem as well as the optimal solution are sought iteratively by applying either a generalized Newton method or a bisection method to the logarithmic-exponential value-estimation function formulation. The convergence properties of the solution algorithms guarantee the identification of an approximate optimal solution of the original problem, up to any predetermined degree of accuracy, within a finite number of iterations.     

Real-Time Solution of Bi-Level Optimal Control Problems

Bi-level optimal control problems are presented as an extension to classical optimal control problems. Hereby, additional constraints for the primary problem are considered, which depend on the optimal solution of a secondary optimal control problem. A demanding problem is the numerical complexity, since at any point in time the solution of the optimal control problem as well as a complete solution of the secondary problem have to be determined. Hence we deal with two dependent variables in time. The numerical solution of the bi-level problem is illustrated by an application of a container crane. Jerk and energy optimal trajectories with free final time are calculated under the terminal condition that the crane system comes to be at rest at a predefined location. In enlargement additional constraints are investigated to ensure that the crane system can be brought to a rest position by a safety stop at a free but admissible location in minimal time from any state of the trajectory. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

传统离散型报童决策模型的一个问题及其改进方案

实践表明 ,对状态变量的递增量较大的报童问题 ,应用传统的离散型报童模型所确定的最优决策量与实际最优方案之间可能存在较大偏差 .分析了这一偏差出现的原因 ,提出了状态变量递增量较大的情况下的离散变量报童模型和求解方法 ,并证明这一模型比原模型得出的求解结果更为精确 .最后 ,通过算例验证了新模型对原模型的改进 .     

Convergence and Error Bound for Perturbation of Linear Programs

In various penalty/smoothing approaches to solving a linear program, one regularizes the problem by adding to the linear cost function a separable nonlinear function multiplied by a small positive parameter. Popular choices of this nonlinear function include the quadratic function, the logarithm function, and the x ln(x)-entropy function. Furthermore, the solutions generated by such approaches may satisfy the linear constraints only inexactly and thus are optimal solutions of the regularized problem with a perturbed right-hand side. We give a general condition for such an optimal solution to converge to an optimal solution of the original problem as the perturbation parameter tends to zero. In the case where the nonlinear function is strictly convex, we further derive a local (error) bound on the distance from such an optimal solution to the limiting optimal solution of the original problem, expressed in terms of the perturbation parameter.     

Computation of the optimal tolls on the traffic network

The present paper is devoted to the computation of optimal tolls on a traffic network that is described as fuzzy bilevel optimization problem. As a fuzzy bilevel optimization problem we consider bilinear optimization problem with crisp upper level and fuzzy lower level. An effective algorithm for computation optimal tolls for the upper level decision-maker is developed under assumption that the lower level decision-maker chooses the optimal solution as well. The algorithm is based on the membership function approach. This algorithm provides us with a global optimal solution of the fuzzy bilevel optimization problem.     

Enumeration of All Possibly Optimal Vertices with Possible Optimality Degrees in Linear Programming Problems with a Possibilistic Objective Function

In this paper, we treat linear programming problems with fuzzy objective function coefficients. To such a problem, the possibly optimal solution set is defined as a fuzzy set. It is shown that any possibly optimal solution can be represented by a convex combination of possibly optimal vertices. A method to enumerate all possibly optimal vertices with their membership degrees is developed. It is shown that, given a possibly optimal extreme point with a higher membership degree, the membership degree of an adjacent extreme point is calculated by solving a linear programming problem and that all possibly optimal vertices are enumerated sequentially by tracing adjacent possibly optimal extreme points from a possibly optimal extreme point with the highest membership degree.     

Robust Solution of Nonconvex Global Optimization Problems

The concept of ɛ-approximate optimal solution as widely used in nonconvex global optimization is not quite adequate, because such a point may correspond to an objective function value far from the true optimal value, while being infeasible. We introduce a concept of essential ɛ-optimal solution, which gives a more appropriate approximate optimal solution, while being stable under small perturbations of the constraints. A general method for finding an essential ɛ-optimal solution in finitely many steps is proposed which can be applied to d.c. programming and monotonic optimization.