向量优化问题中的弱S-有效解

在局部凸拓扑线性空间中, 提出了集值向量优化问题的弱S-有效解和S-次似凸性概念. 在S-次似凸性假设下建立了择一性定理, 并利用择一性定理建立了弱S-有效解的标量化定理. 此外, 通过几个具体例子解释了主要结果.     

非线性弱连续映射的二择一定理及其应用

本文对弱连续算子方程建立了二择一定理.利用该定理,我们可以对拟线性椭圆 Dirichlet 问题特征问题进行讨论.对于局部可解性与方程的非平凡解的存在性二者之间,断言其中必有一个成立.     

择一性与多目标优化

众所周知,线性规划和非线性规划理论中的一些主要定理·(例如最优性条件、对偶性和鞍点定理等),可以统一地由关于两组线性不等式相容的择一性定理推导而得.Giannessi 提出,用广义的函数不等式组相容的择一性定理来统一地处理多目标(向量)优化理论中的一些主要定理,但这种推广还不足以处理不可微规划的最优性条件.本文用集值映射的概念提出两组广义不等式系统的择一性,从而统一处理了多目标规划非     

择一定理在离散时间线性时不变系统中的应用

基于广义择一定理,可以讨论离散时间线性时不变系统中的若干问题.首先可以利用广义择一定理得出Layaponov不等式的可行性与系统矩阵特征值的若干关系.其次利用这种广义择一定理讨论Ricaati不等式解的存在性,由此给出更-般KYP引理的简洁证明.     

一类向量极值问题的最优性条件

本文利用序线性空间中关于次似凸集值映射的择一性定理,得出了具有广义等式和不等式约束的向量极值问题的最优性条件.     

向量优化问题的线性标量化方法和Lagrange乘子研究

向量优化是数学规划一个重要分支,其理论与方法不仅与很多学科有密切联系,而且在新兴的多学科交叉领域中有着广泛的应用.本文从向量值广义凸映射、择一定理、线性标量化方法和Lagrange乘子存在性定理等4个方面对这一领域的研究进展情况及所用方法作了较为系统的总结.首先,介绍基于像空间方法的一类广义凸向量值映射和集值映射,总结已有的广义凸映射之间的关系.其次,介绍线性系统下择一定理到非线性系统下择一定理的发展,重点总结凸性或广义凸性条件下的择一定理研究.同时,针对择一定理的应用,给出向量优化问题各种解在凸或广义凸性条件下的线性标量化方法,进而总结向量优化问题的解,特别是真有效解的Lagrange乘子存在性结果.     

时滞不确定中立型分布参数系统的稳定性

针对一类时滞不确定中立型分布参数系统,研究该系统基于线性矩阵不等式方法的稳定性判据.基于线性矩阵不等式(LMI)方法,通过构造一系列适当的李雅普诺夫函数,利用散度定理和矩阵不等式技术,给出了系统是渐近稳定的充分条件.充分条件要求满足两个线性矩阵不等式,而线性矩阵不等式容易利用Matlab中的LMI工具箱进行求解.最后,数值算例验证了该方法的有效性.     

集值优化导数型最优性条件

借助于相依上导数的概念,建立了锥次类凸集值映射的导数型择一性定理,并利用择一性定理获得了集值优化导数型的最优性必要条件和充分条件.     

LMI优化的一种原对偶中心路径算法

系统和控制理论中许多重要的问题,都可转化为具有线性目标函数、线性矩阵不等式约束的LMI优化问题,从而使其在数值上易于求解.本文给出一种求解LMI优化问题的原对偶中心路径算法,该算法利用牛顿方法求解中心路径方程得到牛顿系统,并将该牛顿系统对称化以避免得到非对称化的搜索方向.文章详细分析了算法的计算复杂性.     

一般化凸空间上截口定理及其对择一不等式的应用

我们根据一般化凸空间上的KKM型定理得到了截口定理,然后作为它的应用讨论了若干个择一不等式.最后,引进了一个具体的一般化凸空间并在该空间上讨论了择一不等式解的存在性问题.     

Duality and saddle-points for convex-like vector optimization problems on real linear spaces

Usually, finite dimensional linear spaces, locally convex topological linear spaces or normed spaces are the framework for vector and multiojective optimization problems. Likewise, several generalizations of convexity are used in order to obtain new results. In this paper we show several Lagrangian type duality theorems and saddle-points theorems. From these, we obtain some characterizations of several efficient solutions of vector optimization problems (VOP), such as weak and proper efficient solutions in Benson’s sense. These theorems are generalizations of preceding results in two ways. Firstly, because we consider real linear spaces without any particular topology, and secondly because we work with a recently appeared convexlike type of convexity. This new type, designated GVCL in this paper, is based on a new algebraic closure which we named vector closure. This research for the second author was partially supported by Ministerio de Ciencia y Tecnología (Spain), project BFM2003-02194.     

Duality Theory in Fuzzy Linear Programming Problems with Fuzzy Coefficients

The concept of fuzzy scalar (inner) product that will be used in the fuzzy objective and inequality constraints of the fuzzy primal and dual linear programming problems with fuzzy coefficients is proposed in this paper. We also introduce a solution concept that is essentially similar to the notion of Pareto optimal solution in the multiobjective programming problems by imposing a partial ordering on the set of all fuzzy numbers. We then prove the weak and strong duality theorems for fuzzy linear programming problems with fuzzy coefficients.     

Generalized Motzkin Theorems of the Alternative and Vector Optimization Problems

In this paper, we introduce a definition of generalized convexlike functions (preconvexlike functions). Then, under the weakened convexity, we study vector optimization problems in Hausdorff topological linear spaces. We establish some generalized Motzkin theorems of the alternative. By use of these theorems of the alternative, we obtain some Lagrangian multiplier theorems. A saddle-point theorem and a scalarization theorem are also derived.Communicated by F. GiannessiThe author thank Ginndomenico Mastrocni for helpful and useful comments.     

Duality in nonlinear multiobjective programming using augmented Lagrangian functions

A vector-valued generalized Lagrangian is constructed for a nonlinear multiobjective programming problem. Using the Lagrangian, a multiobjective dual is considered. Without assuming differentiability, weak and strong duality theorems are established using Pareto efficiency.The research of the second author was partially supported a GTE/SLU grant while visiting St. Lawrence University in the summer of 1991.     

A new class of alternative theorems for SOS-convex inequalities and robust optimization

In this paper, we present a new class of alternative theorems for SOS-convex inequality systems without any qualifications. This class of theorems provides an alternative equations in terms of sums of squares to the solvability of the given inequality system. A strong separation theorem for convex sets, described by convex polynomial inequalities, plays a key role in establishing the class of alternative theorems. Consequently, we show that the optimal values of various classes of robust convex optimization problems are equal to the optimal values of related semidefinite programming problems (SDPs) and so, the value of the robust problem can be found by solving a single SDP. The class of problems includes programs with SOS-convex polynomials under data uncertainty in the objective function such as uncertain quadratically constrained quadratic programs. The SOS-convexity is a computationally tractable relaxation of convexity for a real polynomial. We also provide an application of our theorem of the alternative to a multi-objective convex optimization under data uncertainty.     

非凸集值优化问题严有效解的强对偶定理

本文研究了非凸集值向量优化的严有效解在两种对偶模型的强对偶问题.利用Lagrange对偶和Mond-Weir对偶原理,获得了如下结果：原集值优化问题的严有效解,在一些条件下是对偶问题的强有效解,并且原问题和对偶问题的目标函数值相等;推广了集值优化对偶理论在锥-凸假设下的相应结果.     

The projective method for solving linear matrix inequalities

Numerous problems in control and systems theory can be formulated in terms of linear matrix inequalities (LMI). Since solving an LMI amounts to a convex optimization problem, such formulations are known to be numerically tractable. However, the interest in LMI-based design techniques has really surged with the introduction of efficient interior-point methods for solving LMIs with a polynomial-time complexity. This paper describes one particular method called the Projective Method. Simple geometrical arguments are used to clarify the strategy and convergence mechanism of the Projective algorithm. A complexity analysis is provided, and applications to two generic LMI problems (feasibility and linear objective minimization) are discussed.     

约束向量优化问题的Fenchel-Lagrange对偶及鞍点

The aim of this paper is to apply a perturbation approach to deal with Fenchel- Lagrange duality based on weak efficiency to a constrained vector optimization problem. Under the stability criterion, some relationships between the solutions of primal problem and the Fenchel-Lagrange duality are discussed. Moreover, under the same condition, two saddle-points theorems are proved.     

A delay-range-dependent uniformly asymptotic stability criterion for a class of nonlinear singular systems

This paper investigates a class of nonlinear singular systems. Based on the Lyapunov functional method and the free-weighting matrix method, a uniformly asymptotic stability criterion in terms of only one simple linear matrix inequality (LMI) is addressed, which guarantees stability for such time-varying delay systems. This LMI can be easily solved by convex optimization techniques. Two examples are given to illustrate the effectiveness of the proposed main results. All these results are expected to be of use in the study of nonlinear singular systems.     

线性空间中集值映射向量优化问题的最优性条件与Lagrangian乘子

本文在广义次似凸性假设下,利用择一性定理,在线性空间中获得了含等式与不等式约式集值向量最优化问题的Kuhn-Tucker型最优性条件及Lagrangian乘子定理。