一类新的Lagrangian乘子法

本文提出了求解光滑不等式约束最优化问题新的乘子法,在增广Lagrangian函数中,使用了新的NCP函数的乘子法．该方法在增广Lagrangian函数和原问题之间存在很好的等价性;同时该方法具有全局收敛性,且在适当假设下,具有超线性收敛率．本文给出了一个有效选择参数C的方法．     

半无限规划的一个非线性Lagrangian对偶模型

本文给出半无限规划的一个对偶罚函数模型,该模型能处理目标函数不是凸函数的情形,从而凸(SIP)对偶为该模型的一个特例.并且,作为罚函数,本模型的罚因子比l1-罚函数要小,这使得算法更可行,最后,给出零对偶间隙证明.     

一类网络最优路径的双向模拟扩散算法

基于模拟扩散算法的基本原理,文中提出了一种双向寻求网络最优路径的扩散算法,并介绍了该算法原理和具体计算过程,验证了该算法的正确性和合理性。该算法具有并行计算的能力,适合于分布式计算机,寻求大型复杂网络的最优路径。     

增广Lagrangian对偶分解方法求解变分不等式系统

本文考虑带约束的变分不等式系统.提出一个基于增广Lagrangian对偶的分解算法,本文给出了算法的收敛性分析.     

带新NCP函数的Lagrangian乘子方法

在经营管理、工程设计、科学研究、军事指挥等方面普遍存在着最优化问题,而实际问题中出现的绝大多数问题都被归纳为非线性规划问题之中。作为带等式、不等式约束的复杂事例,最优化问题的求解向来较为繁琐、困难。适当条件下,非线性互补函数(NCP)可以与约束优化问题相结合,其中NCP函数的无约束极小解对应原约束问题的解及其乘子。本文提出了一类新的NCP函数用于解决等式和不等式约束非线性规划问题,结合新的NCP函数构造了增广Lagrangian函数。在适当假设条件下,证明了增广Lagrangian函数与原问题的解之间的一一对应关系。同时构造了相应算法,并证明了该算法的收敛性和有效性。     

一类随机规划逼近最优值和最优解集的收敛性

本文提出强上图收敛的概念,讨论了逼近随机规划的目标函数序列的强上图收敛性,研究了逼近随机规划最优值和最优解集的收敛性条件,得到了一类随机规划逼近最优值和最优解集的收敛性.     

一类资源最优配置的充要条件及其应用

本文研究了由工业投资、教育投资等问题中导出的一类非线性规划问题,应用Kuhn-Tucher定理得到了Rn中向量x=(x1,x2,…,xn)是这问题最优解的充分必要条件．应用这一结果导出了求解一类资源最优配置问题的新算法．这是一个具有计算复杂度为O(mn(m n))的多项式型算法．     

一类极大极小随机规划逼近问题最优解集的上半收敛性

本文首先将极大极小随机规划等价的转化为一个二层随机规划,在下层初始随机规划最优解集为多点集的情形下,给出下层随机规划逼近问题最优解集集值映射关于上层决策变量参数的上半收敛性和最优值函数的连续性.然后将上层随机规划等价转化为以上层和下层决策变量作为整体决策变量,以下层规划最优解集的图作为约束条件的单层规划,并在下层初始随机规划最优解集的图为正则的条件下,得到上层随机规划逼近问题最优解集关于最小信息概率度量收敛的上半收敛性.     

散乱数据多元最优插值的误差估计及其超收敛性——带离散边界条件

在[1]中,李岳生讨论了空间H~(m,n)(R)上带离散边界条件散乱数据多元最优插值,给出了最优插值的存在唯一性定理、特征性质及其结构,并给出了解的构造方法。本文讨论当m=n=1时散乱数据多元最优插值,给出了某些情形插值的误差估计,并且发现最优插值在某些点上还具有超收敛性。     

一类概率约束规划逼近最优解集的上半收敛性

对一类概率约束规划逼近最优解集的上半收敛性进行了研究.利用概率测度弱收敛的特征,给出了概率约束规划可行集的收敛性条件,得到了概率约束规划逼近最优解集的上半收敛性.     

On the Continuity of the Optimal Value in Parametric Linear Optimization: Stable Discretization of the Lagrangian Dual of Nonlinear Problems

This paper is focused on the stability of the optimal value, and its immediate repercussion on the stability of the optimal set, for a general parametric family of linear optimization problems in ⁿ. In our approach, the parameter ranges over an arbitrary metric space, and each parameter determines directly a set of coefficient vectors describing the linear system of constraints. Thus, systems associated with different parameters are not required to have the same number (cardinality) of inequalities. In this way, discretization techniques for solving a nominal linear semi-infinite optimization problem may be modeled in terms of suitable parametrized problems. The stability results given in the paper are applied to the stability analysis of the Lagrangian dual associated with a parametric family of nonlinear programming problems. This dual problem is translated into a linear (semi-infinite) programming problem and, then, we prove that the lower semicontinuity of the corresponding feasible set mapping, the continuity of the optimal value function, and the upper semicontinuity of the optimal set mapping are satisfied. Then, the paper shows how these stability properties for the dual problem entail a nice behavior of parametric approximation and discretization strategies (in which an ordinary linear programming problem may be considered in each step). This approximation–discretization process is formalized by means of considering a double parameter: the original one and the finite subset of indices (grid) itself. Finally, the convex case is analyzed, showing that the referred process also allows us to approach the primal problem.Mathematics Subject Classifications (2000) Primary 90C34, 90C31; secondary 90C25, 90C05.     

Subdifferentiability and the Duality Gap

We point out a connection between sensitivity analysis and the fundamental theorem of linear programming by characterizing when a linear programming problem has no duality gap. The main result is that the value function is subdifferentiable at the primal constraint if and only if there exists an optimal dual solution and there is no duality gap. To illustrate the subtlety of the condition, we extend Kretschmer's gap example to construct (as the value function of a linear programming problem) a convex function which is subdifferentiable at a point but is not continuous there. We also apply the theorem to the continuum version of the assignment model.     

一类非光滑优化问题的最优性与对偶（英文）

本文研究了一类带等式和不等式约束的非光滑多目标优化问题,给出了该类问题的Karush-Kuhn-Tucker最优性必要条件和充分条件,建立了该类规划问题的一类混合对偶模型的弱对偶定理、强对偶定理、逆对偶定理、严格逆对偶定理和限制逆对偶定理.     

Some Results about Duality and Exact Penalization

In this paper, we introduce the concept of the valley at 0 augmenting function and apply it to construct a class of valley at 0 augmented Lagrangian functions. We establish the existence of a path of optimal solutions generated by valley at 0 augmented Lagrangian problems and its convergence toward the optimal set of the original problem and obtain the zero duality gap property between the primal problem and the valley at 0 augmented Lagrangian dual problem. Moreover, we establish the exact penalization representation results in the framework of valley at 0 augmented Lagrangian.     

On the Existence and Convergence of the Central Path for Convex Programming and Some Duality Results

This paper gives several equivalent conditions which guarantee the existence of the weighted central paths for a given convex programming problem satisfying some mild conditions. When the objective and constraint functions of the problem are analytic, we also characterize the limiting behavior of these paths as they approach the set of optimal solutions. A duality relationship between a certain pair of logarithmic barrier problems is also discussed.     

一种求解路径优化问题的新型人工鱼群算法

针对人工鱼群算法由于固定视野导致寻优效率低、易陷入局部极值的弊端,引入视野递减反馈策略,提出一种改进人工鱼群算法.视野随着迭代次数和寻优反馈信息适时变化,旨在平衡算法的全局搜索和局部搜索能力.实验测试表明算法在保证收敛速度的基础上提高了计算精度,并且增加了算法陷入局部极值时快速跳出的可能性,最后将改进算法应用于求解国家AAAAA级风景区最短遍历路径问题.     

解一类Hessian矩阵亏秩的修正BFGs算法及其局部Q-超线性收敛性

本文对凸函数在极值点的Hessian矩阵是秩亏一的情况下,给出了一类求解无约束优化问题的修正BFGS算法.算法的思想是对凸函数加上一个修正项,得到一个等价的模型,然后简化此模型得到一个修正的BFGS算法.文中证明了该算法是一个具有超线性收敛的算法,并且把修正的BFGS算法同Tensor方法进行了数值比较,证明了该算法对求解秩亏一的无约束优化问题更有效.     

紧急疏散中最优抗出错路径选择模型与算法

疏散路径选择是紧急疏散中的重要问题,为了减小疏散人在紧急疏散过程中由于路径选择错误带来的损失,提出一对起讫点间最优抗出错路径选择模型。给出路径出错系数的定义,用以度量疏散人路径选择错误带来的疏散效率损失,并且设计了求解最优抗出错路径的DAE算法,证明该算法的时间复杂度为O（ mn2）。结果表明,选择最优抗出错路径作为疏散路径,能够有效地抵抗由于疏散人路径选择错误带来的损失,对提高突发事件下的疏散效率具有实际意义。     

NCP Functions Applied to Lagrangian Globalization for the Nonlinear Complementarity Problem

Based on NCP functions, we present a Lagrangian globalization (LG) algorithm model for solving the nonlinear complementarity problem. In particular, this algorithm model does not depend on some specific NCP function. Under several theoretical assumptions on NCP functions we prove that the algorithm model is well-defined and globally convergent. Several NCP functions applicable to the LG-method are analyzed in details and shown to satisfy these assumptions. Furthermore, we identify not only the properties of NCP functions which enable them to be used in the LG method but also their properties which enable the strict complementarity condition to be removed from the convergence conditions of the LG method. Moreover, we construct a new NCP function which possesses some favourable properties.     

一类新的共轭梯度法的全局收敛性

本文提出了一类与HS方法相关的新的共轭梯度法.在强Wolfe线搜索的条件下,该方法能够保证搜索方向的充分下降性,并且在不需要假设目标函数为凸的情况下,证明了该方法的全局收敛性.同时,给出了这类新共轭梯度法的一种特殊形式,通过调整参数ρ,验证了它对给定测试函数的有效性.