一类非凸全局最优化问题的最优性条件

本文研究一类非凸连续全局最优化问题的最优性条件.通过构造含有参数的辅助函数,且对辅助函数作极限运算,得到一种基于积分运算的积分型全局最优性条件,并利用该辅助函数得到非凸规划问题全局最优解的一些充分必要条件.     

不连续全局优化问题的对数变差积分途径

针对不连续无约束全局优化问题,构造且运用对数变差积分来进行研究和求解.具体给出了对数变差积分函数的分析性质及其全局优化问题的最优性条件和概念性算法.结合Monte-Carlo技术,特别针对n=100个变量、具有不连续目标函数的三个具体实例进行了数值试验,计算结果也表明所给方法的可行性和有效性.     

一类变结构动态系统的非光滑最优性条件

研究了一类事件驱动的变结构动态系统的非光滑最优性条件. 通过引入一个新的时间变量, 将变结构动态系统的最优性问题转化为古典动态系统的最优性问题. 基于广义微分和古典动态系统的最优性理论, 得到了该系统的Frechet上微分形式的必要性条件, 推广了已有文献的相关结论. 结果表明, 在系统的连续运行过程中, 控制变量、协态变量和状态变量满足最小值原理和协态方程. 在系统的运行模型发生改变时, 协态变量产生一定的跳跃, 哈密尔顿函数连续. 最后通过一个算例说明了该结论的有效性.     

渐近最优的经验贝叶斯决策

本文在加权线性损失下讨论一类广义指数分布刻度参数的经验贝叶斯检验问题.利用核密度估计函数构造单调的经验贝叶斯检验函数,在适当的条件下证明所构造的检验函数的渐近最优性并获得其收敛速度.该收敛速度可以任意接近O(n-1).最后,给出一个例子用以验证本文的主要结果是合理的.     

混合整数二次规划问题的全局最优性条件

本文给出了混合整数二次规划问题的全局最优性条件,包括全局最优充分性条件和全局最优必要性条件.我们还给出了一个数值实例用以说明如何利用本文所给出的全局最优性条件来判定一个给定点是否是全局最优解.     

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。     

非光滑向量均衡问题近似拟全局真有效解的最优性条件

本文研究一类非光滑向量均衡问题(Vector Equilibrium Problem)(VEP)关于近似拟全局真有效解的最优性条件.首先,利用凸集的拟相对内部型分离定理和Clarke次微分的性质,得到了问题(VEP)关于近似拟全局真有效解的最优性必要条件.其次,引入近似伪拟凸函数的概念,并给出具体实例验证其存在性,且在该凸性假设下建立了问题(VEP)关于近似拟全局真有效解的充分条件.最后,利用Tammer函数以及构建满足一定性质的非线性泛函,得到了问题(VEP)近似拟全局真有效解的标量化定理.     

非线性规划问题的全局最优必要条件

主要探讨优化问题的全局最优性条件.对于目标函数和不等式约束为一般二次可微函数的非线性规划问题,构造了相应的多项式下估计函数,使之满足多项式择一定理的条件,从而得到全局最优的必要条件.     

薄体结构温度场的高阶边界元分析

分析了二维问题边界元法3节点二次单元的几何特征,区分和定义了源点相对高阶单元的Ⅰ型和Ⅱ型接近度.针对二维位势问题高阶边界元中奇异积分核,构造出具有相同Ⅱ型几乎奇异性的近似核函数,在几乎奇异积分单元上分离出积分核中主导的奇异函数部分.原积分核扣除其近似核函数后消除几乎奇异性,成为正则积分核函数,并采用常规Gauss数值方法计算该正则积分;对奇异核函数的积分推导出解析公式,从而建立了一种新的边界元法高阶单元几乎奇异积分半解析算法.应用该算法计算了二维薄体结构温度场算例,计算结果表明高阶单元半解析算法能充分发挥边界元法优势,显著提高计算精度.     

函数型二次模型的假设检验

本文研究了函数型二次回归中二次参数函数的显著性检验问题。采用函数型主成分分析将预测变量函数进行投影降维,利用零模型和全模型的残差平方和构造F型检验统计量。在一定的正则条件下证明了检验统计量在原假设下渐近于F分布,在备择假设下检验统计量依概率趋于无穷,从而表明该检验方法是相合的。进一步证明了在一定收敛速度的局部备择假设下,检验统计量渐近于非中心F分布。最后通过数值模拟研究了该检验方法在有限样本下的表现,并给出了一个实际例子进一步验证所提方法的有效性。     

Global Optimality Conditions and Optimization Methods for Quadratic Knapsack Problems

The quadratic knapsack problem (QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for (QKP), which include necessary conditions and sufficient conditions. Then a local optimization method for (QKP) is developed using the necessary global optimality condition. Finally a global optimization method for (QKP) is proposed based on the sufficient global optimality condition, the local optimization method and an auxiliary function. Several numerical examples are given to illustrate the efficiency of the presented optimization methods.     

An Optimization Problem with a Separable Non-Convex Objective Function and a Linear Constraint

For a class of global optimization (maximization) problems, with a separable non-concave objective function and a linear constraint a computationally efficient heuristic has been developed.The concave relaxation of a global optimization problem is introduced. An algorithm for solving this problem to optimality is presented. The optimal solution of the relaxation problem is shown to provide an upper bound for the optimal value of the objective function of the original global optimization problem. An easily checked sufficient optimality condition is formulated under which the optimal solution of concave relaxation problem is optimal for the corresponding non-concave problem. An heuristic algorithm for solving the considered global optimization problem is developed.The considered global optimization problem models a wide class of optimal distribution of a unidimensional resource over subsystems to provide maximum total output in a multicomponent systems.In the presented computational experiments the developed heuristic algorithm generated solutions, which either met optimality conditions or had objective function values with a negligible deviation from optimality (less than 1/10 of a percent over entire range of problems tested).     

箱式约束变分不等式的一类新光滑gap函数

针对箱式约束变分不等式问题,利用一类积分型全局最优性条件,提出了一个新光滑gap函数．该光滑gap函数形式简单且具有较好的性质．利用该gap函数,箱式约束变分不等式可转化为等价光滑优化问题进行求解．进一步地,讨论了可保证等价光滑优化问题的任意聚点为箱式约束变分不等式问题解的条件．以一个简单的摩擦接触问题为例阐释了该方法的应用．最后,利用标准的变分不等式考题验证了方法的有效性．     

线性约束三次规划问题的全局最优性必要条件和最优化算法

讨论了带线性不等式约束三次规划问题的最优性条件和最优化算法. 首先, 讨论了带有线性不等式约束三次规划问题的 全局最优性必要条件. 然后, 利用全局最优性必要条件, 设计了解线性约束三次规划问题的一个新的局部最优化算法(强局部最优化算法). 再利用辅助函数和所给出的新的局部最优化算法, 设计了带有线性不等式约束三 规划问题的全局最优化算法. 最后, 数值算例说明给出的最优化算法是可行的、有效的.     

Global optimality conditions and optimization methods for quadratic integer programming problems

In this paper, we first establish some sufficient and some necessary global optimality conditions for quadratic integer programming problems. Then we present a new local optimization method for quadratic integer programming problems according to its necessary global optimality conditions. A new global optimization method is proposed by combining its sufficient global optimality conditions, local optimization method and an auxiliary function. The numerical examples are also presented to show that the proposed optimization methods for quadratic integer programming problems are very efficient and stable.     

Successive Optimization Method via Parametric Monotone Composition Formulation*

In this paper a successive optimization method for solving inequality constrained optimization problems is introduced via a parametric monotone composition reformulation. The global optimal value of the original constrained optimization problem is shown to be the least root of the optimal value function of an auxiliary parametric optimization problem, thus can be found via a bisection method. The parametric optimization subproblem is formulated in such a way that it is a one-parameter problem and its value function is a monotone composition function with respect to the original objective function and the constraints. Various forms can be taken in the parametric optimization problem in accordance with a special structure of the original optimization problem, and in some cases, the parametric optimization problems are convex composite ones. Finally, the parametric monotone composite reformulation is applied to study local optimality.     

A sufficient and necessary condition for global optimization

We consider a class of global optimization problems, in which the objective function is Lebesgue integrable. In this paper, we present a sufficient and necessary condition for computing the essential infimum. This optimality condition can be used to design algorithms for solving the global optimization problem.     

A new local and global optimization method for mixed integer quadratic programming problems

In this paper, a new local optimization method for mixed integer quadratic programming problems with box constraints is presented by using its necessary global optimality conditions. Then a new global optimization method by combining its sufficient global optimality conditions and an auxiliary function is proposed. Some numerical examples are also presented to show that the proposed optimization methods for mixed integer quadratic programming problems with box constraints are very efficient and stable.     

积分型总极值方法的最优性条件

郑权提出了求总极值问题的积分—水平集的概念性算法,同时给出了最优性条件.本文构造函数F(x),讨论了该函数的性质,证明求解原问题等价于求解方程F(c)=0的根.在文中给出了相应的总极值存在的最优性条件.     

Risk-sensitive average continuous-time Markov decision processes with unbounded rates

Abstract

In this paper we study the risk-sensitive average cost criterion for continuous-time Markov decision processes in the class of all randomized Markov policies. The state space is a denumerable set, and the cost and transition rates are allowed to be unbounded. Under the suitable conditions, we establish the optimality equation of the auxiliary risk-sensitive first passage optimization problem and obtain the properties of the corresponding optimal value function. Then by a technique of constructing the appropriate approximating sequences of the cost and transition rates and employing the results on the auxiliary optimization problem, we show the existence of a solution to the risk-sensitive average optimality inequality and develop a new approach called the risk-sensitive average optimality inequality approach to prove the existence of an optimal deterministic stationary policy. Furthermore, we give some sufficient conditions for the verification of the simultaneous Doeblin condition, use a controlled birth and death system to illustrate our conditions and provide an example for which the risk-sensitive average optimality strict inequality occurs.