凸二次-线性双层规划的共轭对偶及其性质

研究具有一般形式的凸二次-线性双层规划问题。讨论了这类双层规划问题的DC规划等价形式,利用DC规划共轭对偶理论,提出了凸二次-线性双层规划的共轭对偶规划,并给出相应的对偶性质。     

非线性规划的Ω共轭对偶理论

本文通过推广凸共轭函数和次梯度的概念,建立了非线性规划问题的一类对偶理论——Ω共轭对偶理论.研究结果表明,许多关于非线性最优化对偶性方面的结论都是本文的特殊情况.     

改进共轭梯度法求解无约束二次凸规划问题

针对共轭梯度法求解无约束二次凸规划时,在构造共轭方向上的局限性,对共轭梯度法进行了改进.给出了构造共轭方向的新方法,利用数学归纳法对新方法进行了证明.同时还给出了改进共轭梯度法在应用时的基本计算过程,并对方法的收敛性进行了证明.通过实例求解,说明了在求解二次无约束凸规划时,该方法相比共轭梯度法具有一定的优势.     

DC分式规划问题的最优性条件(英文)

本文主要研究约束DC分式规划问题.通过借助共轭函数上图的性质,首先讨论了约束DC分式规划问题的全局最优解和局部最优解的必要条件或充分条件.作为应用,还阐明了许多关于优化问题的结果可以作为本文的特殊情况.     

两类线性双层规划的算法

根据值型线性双层规划的 Johri一般对偶的对偶性质 ,把对两类值型线性双层规划的求解问题转化为对有限个线性规划的求解问题 ,简化了双层规划的求解过程 ,给出了求解这两类值型线性双层规划的一种有效算法     

某类等式约束二次规划问题的一个共轭方向法

本文提出了一种求解某类等式约束二次规划问题的一个共轭方向迭代法，并给出了算法的有限终止性证明．同时我们把此算法推广到不等式约束二次规划问题中，从而得到了一种求解不等式约束二次规划问题的算法．     

DC规划的一个对偶定理

本文给出了DC规划的直接对偶定理和逆对偶定理。作为特例,它们蕴涵了符号几何规划的对偶定理,最后给出一个数值例子来说明定理。1.引言     

非线性规划的(H,Ω)对偶理论

对偶理论是非线性规划理论的一个重要组成部分,目前较成熟和完善的仅是凸规划的对偶理论.对于非凸规划对偶问题的研究仅有少量的工作完成,其结果也不令人满意.文献[1]就凸共轭函数进行了推广,建立了(H,(?))共轭函数理论,这一理论为凸对偶向非凸对偶迈进提供了基础.本文应用这一(H,(?))共轭函数理论,提出并建立了非线性规划的(H,(?))对偶理论.应用表明,在特殊簇 H 及(?)下,迄今为止几乎所有非线性规划的对偶理论都是这一对偶框架下的特殊形式,因此可以说,它是对偶理论的一个突破.     

求解线性不等式组的方法

本提出了一个新的求解线性不等式组可行解的方法－－无约束极值方法。通过在线性不等式组的非空可行域的相对内域上建立一个非线性极值问题，根据对偶关系，得到了一个对偶空间的无约束极值及原始，对偶变量之间的简单线性映射关系，这样将原来线性不等式组问题的求解转化为一个无约束极值问题。中主要讨论了求解无约束极值问题的共轭梯度算法。同时，在寻找不等式组可行解的过程中，定义了穿越方向，这样大大减少计算量。中最后数值实验结果表明此算法是有效的。     

向量集值映射的共轭对偶

借助抽象算子将共轭映射的概念的到抽象空间,引入了集值映射的共轭映射和次梯度,据此讨论了集值映射共轭对偶的全局稳定性。     

带有极值函数的多目标双层规划问题的对偶

For a multiobjective bilevel programming problem(P) with an extremal-value function,its dual problem is constructed by using the Fenchel-Moreau conjugate of the functions involved.Under some convexity and monotonicity assumptions,the weak and strong duality assertions are obtained.     

Regularity conditions characterizing Fenchel–Lagrange duality and Farkas-type results in DC infinite programming

In this paper, we consider a DC infinite programming problem (P) with inequality constraints. By using the properties of the epigraph of the conjugate functions, we introduce some new notions of regularity conditions for (P). Under these new regularity conditions, we completely characterize the Fenchel–Lagrange duality and the stable Fenchel–Lagrange duality for (P). Similarly, we also completely characterize the Farkas-type results and the stable Farkas-type results for (P). As applications, we obtain the corresponding results for conic programming problems.     

广义几何规划的压缩共轭梯度路径非单调信赖域算法(英文)

In this paper,on the basis of making full use of the characteristics of unconstrained generalized geometric programming（GGP）,we establish a nonmonotonic trust region algorithm via the conjugate path for solving unconstrained GGP problem.A new type of condensation problem is presented,then a particular conjugate path is constructed for the problem,along which we get the approximate solution of the problem by nonmonotonic trust region algorithm,and further prove that the algorithm has global convergence and quadratic convergence properties.     

Lagrange-type duality in DC programming

We show a Lagrange-type duality theorem for a DC programming problem, which is a generalization of previous results by J.-E. Martínez-Legaz, M. Volle [5] and Y. Fujiwara, D. Kuroiwa [1] when all constraint functions are real-valued. To the purpose, we decompose the DC programming problem into certain infinite convex programming problems.     

On duality theory of convex semi-infinite programming

In this article we discuss weak and strong duality properties of convex semi-infinite programming problems. We use a unified framework by writing the corresponding constraints in a form of cone inclusions. The consequent analysis is based on the conjugate duality approach of embedding the problem into a parametric family of problems parameterized by a finite-dimensional vector.     

Stable zero duality gaps in convex programming: Complete dual characterisations with applications to semidefinite programs

The zero duality gap that underpins the duality theory is one of the central ingredients in optimisation. In convex programming, it means that the optimal values of a given convex program and its associated dual program are equal. It allows, in particular, the development of efficient numerical schemes. However, the zero duality gap property does not always hold even for finite-dimensional problems and it frequently fails for problems with non-polyhedral constraints such as the ones in semidefinite programming problems. Over the years, various criteria have been developed ensuring zero duality gaps for convex programming problems. In the present work, we take a broader view of the zero duality gap property by allowing it to hold for each choice of linear perturbation of the objective function of the given problem. Globalising the property in this way permits us to obtain complete geometric dual characterisations of a stable zero duality gap in terms of epigraphs and conjugate functions. For convex semidefinite programs, we establish necessary and sufficient dual conditions for stable zero duality gaps, as well as for a universal zero duality gap in the sense that the zero duality gap property holds for each choice of constraint right-hand side and convex objective function. Zero duality gap results for second-order cone programming problems are also given. Our approach makes use of elegant conjugate analysis and Fenchel's duality.     

关于二次函数的约束优化的算法研究

在本篇论文中,我们尝试用共轭方向法来处理二次函数的约束优化问题．我们 首先讨论了一下正定时的情况,再讨论负定时的情况．对于正定二次函数的优化问题,我 们提出一个算法,可以构造一列收敛到最优点的数列．对于负定二次函数的优化问题,我 们给出了一些结果．