一类非凸区域的拟法锥构造方法及其在非凸规划求解中的应用

本文给出基于球形的一类满足拟法锥条件区域的拟法锥构造方法,基于该可行域的拟法锥,建立求解在该类非凸区域上的规划问题的K-K-T点的部分凝聚同伦组合方程,并证明了该同伦内点法的整体收敛性,给出实现同伦内点法的具体数值跟踪算法步骤,并通过数值例子证明算法是可行的和有效的．     

二阶锥规划两个新的预估-校正算法

基于不可行内点法和预估-校正算法的思想,提出两个新的求解二阶锥规划的内点预估-校正算法．其预估方向分别是Newton方向和Euler方向,校正方向属于Alizadeh-Haeberly-Overton(AHO)方向的范畴．算法对于迭代点可行或不可行的情形都适用．主要构造了一个更简单的中心路径的邻域,这是有别于其它内点预估-校正算法的关键．在一些假设条件下,算法具有全局收敛性、线性和二次收敛速度,并获得了O(rln(ε0/ε))的迭代复杂性界,其中r表示二阶锥规划问题所包含的二阶锥约束的个数．数值实验结果表明提出的两个算法是有效的．     

解新锥模型信赖域子问题的折线法

本文以新锥模型信赖域子问题的最优性条件为理论基础,认真讨论了新子问题的锥函数性质,分析了此函数在梯度方向及与牛顿方向连线上的单调性.在此基础上本文提出了一个求解新锥模型信赖域子问题折线法,并证明了这一子算法保证解无约束优化问题信赖域法全局收敛性要满足的下降条件.本文获得的数值实验表明该算法是有效的.     

无约束最优化锥模型拟牛顿信赖域方法的收敛性(英)

本文研究无约束最优化雄模型拟牛顿信赖域方法的全局收敛性．文章给出了确保这类方法全局收敛的条件．文章还证明了，当用拆线法来求这类算法中锥模型信赖域子问题的近似解时，确保全局收敛的条件得到满足     

弱拟法锥条件下非凸优化问题的同伦算法

本文给出弱拟法锥条件的定义,并针对非线性组合同伦方程,得到在弱拟法锥条件下求解约束非凸优化问题的同伦内点算法.证明了该算法对于可行域的某个子集中几乎所有的点,同伦路径存在,并且同伦路径收敛于问题的K-K-T点,通过数值例子验证了该算法是有效的.     

一类复杂非凸区域的拟法锥构造方法及其在非凸规划求解中的应用

本文针对基于一般的凸集与"契型"的余集相交形成的一类满足拟法锥条件的复杂非凸区域,给出一种拟法锥的构造方法,在给定的拟法锥条件下,建立求解在该类非凸区域上规划问题的K-K-T点的组合同伦方程,并证明了该同伦内点法的整体收敛性,并通过数值例子证明算法是可行的和有效的.     

基于锥模型的一般信赖域算法收敛性分析

本文给出了锥模型信赖域算法的一般模型，它不仅包含通常的信赖域算法一相当于锥模型算法中bk＝0的情形，而且文献[1]的算法也可看作其子类．我们研究这个模型的较强的全局收敛性，并讨论保证算法具有超线性收敛速率的条件，从而推广了文[1]和文[4]中的若干结果．     

解非凸规划问题动边界组合同伦方法

本文给出了一个新的求解非凸规划问题的同伦方法,称为动边界同伦方程,并在较弱的条件下,证明了同伦路径的存在性和大范围收敛性．与已有的拟法锥条件、伪锥条件下的修正组合同伦方法相比,同伦构造更容易,并且不要求初始点是可行集的内点,因此动边界组合同伦方法比修正组合同伦方法及弱法锥条件下的组合同伦内点法和凝聚约束同伦方法更便于应用．     

基于一个有限罚函数的二阶锥优化的原始-对偶内点算法

本文基于一个有限罚函数,设计了关于二阶锥优化问题的原始-对偶路径跟踪内点算法,由于该罚函数在可行域的边界取有限值,因而它不是常规的罚函数,尽管如此,它良好的解析性质使得我们能分析算法并得到基于大步校正和小步校正方法目前较好的多项式时间复杂性分别为O(N~(1/2)log N log N/ε)和O(N~(1/2)log N/ε),其中N为二阶锥的个数．     

非线性二阶锥互补问题的低阶罚函数算法（英文）

本文研究非线性二阶锥互补问题的一般低阶罚函数算法.并将非线性二阶锥互补问题转化为序列非线性方程组.在一定条件下,当罚因子趋向于无穷时,获得序列非线性方程组的解序列以指数速度收敛于原始非线性二阶锥互补问题的解,推广了幂罚函数算法求解非线性二阶锥互补问题的结果.数值实验结果说明了算法的有效性.     

Optimality conditions for vector optimization problem governed by the cone constrained generalized equations

ABSTRACT

The paper considers a class of vector optimization problems with cone constrained generalized equations. By virtue of advanced tools of variational analysis and generalized differentiation, a limiting normal cone of the graph of the normal cone constrained by the second-order cone is obtained. Based on the calmness condition, we derive an upper estimate of the coderivative for a composite set-valued mapping. The necessary optimality condition for the problem is established under the linear independent constraint qualification. As a special case, the obtained optimality condition is simplified with the help of strict complementarity relaxation conditions. The numerical results on different examples are given to illustrate the efficiency of the optimality conditions.     

锥度量空间中c-距离下的不动点定理

在锥度量空间中,用压缩性函数代替具体实数,获得了c-距离下的映射的新的不动点定理.所得结果在条件上不要求映射的非减性,且第一个定理去掉了锥的正规性,第二个定理去掉了映射的连续性,改进了原有的许多重要结论,并给出了相应的例子.     

Cone Order Association

Cone order association, which generalizes the notion of association, is defined. For cone order association, functions which are nondecreasing with respect to a cone order, are nonnegatively correlated. Properties and sufficient conditions are developed. The main result yields a necessary and sufficient condition for a normal random vector to be cone order associated. This parallels a result of L. D. Pitt (1982, Ann. Probab.10 495-499). Cone order association has applications, especially in the area of hypothesis testing. An indication of how cone order association is used to establish unbiasedness of tests is given, along with some examples.     

集值映射多目标规划的K-T最优性条件

讨论集值映射多目标规划(VP)的最优性条件问题.首先,在没有锥凹的假设下,利用集值映射的相依导数,得到了(VP)的锥--超有效解要满足的必要条件和充分条件.其次,在锥凹假设和比推广了的Slater规格更弱的条件下,给出了(VP)关于锥--超有效解的K--T型最优性必要条件和充分条件.     

First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints

In this paper we consider a mathematical program with semidefinite cone complementarity constraints (SDCMPCC). Such a problem is a matrix analogue of the mathematical program with (vector) complementarity constraints (MPCC) and includes MPCC as a special case. We first derive explicit formulas for the proximal and limiting normal cone of the graph of the normal cone to the positive semidefinite cone. Using these formulas and classical nonsmooth first order necessary optimality conditions we derive explicit expressions for the strong-, Mordukhovich- and Clarke- (S-, M- and C-)stationary conditions. Moreover we give constraint qualifications under which a local solution of SDCMPCC is a S-, M- and C-stationary point. Moreover we show that applying these results to MPCC produces new and weaker necessary optimality conditions.     

Quasi-maximum likelihood estimation in GARCH processes when some coefficients are equal to zero

The asymptotic distribution of the quasi-maximum likelihood (QML) estimator is established for generalized autoregressive conditional heteroskedastic (GARCH) processes, when the true parameter may have zero coefficients. This asymptotic distribution is the projection of a normal vector distribution onto a convex cone. The results are derived under mild conditions. For an important subclass of models, no moment condition is imposed on the GARCH process. The main practical implication of these results concerns the estimation of overidentified GARCH models.     

Subdifferential of Optimal Value Functions in Nonlinear Infinite Programming

This paper presents an exact formula for computing the normal cones of the constraint set mapping including the Clarke normal cone and the Mordukhovich normal cone in infinite programming under the extended Mangasarian-Fromovitz constraint qualification condition. Then, we derive an upper estimate as well as an exact formula for the limiting subdifferential of the marginal/optimal value function in a general Banach space setting.     

Normality and Nuclearity of Convex Cones

We give here some characterizations for normality and nuclearity of convex cones. We obtain a sufficient condition for weakly normal cone to be normal (respectively nuclear). Work partially supported by FNARS, Project 003.     

Restricted Robinson Constraint Qualification and Optimality for Cardinality-Constrained Cone Programming

In this paper, optimality conditions are presented and analyzed for the cardinality-constrained cone programming arising from finance, statistical regression, signal processing, etc. By introducing a restricted form of (strict) Robinson constraint qualification, the first-order optimality conditions for the cardinality-constrained cone programming are established based upon the properties of the normal cone. After characterizing further the second-order tangent set to the cardinality-constrained system, the second-order optimality conditions are also presented under some mild conditions. These proposed optimality conditions, to some extent, enrich the optimization theory for noncontinuous and nonconvex programming problems.