不等式组与变分不等式的极大熵函数方法

利用极大熵函数方法将不等式组及变分不等式的求解问题转化为近似可微优化问题，给出了不等式组及变分不等式问题近似解的可微优化方法，得到了不等式组和变分不等式问题的解集合的示性函数．     

广义Minty 向量似变分不等式解的性质

在拓扑向量空间中讨论下Dini方向导数形式的广义Minty向量似变分不等式问题. 可微形式的Minty变分不等式、Minty似变分不等式和Minty向量变分不等式是其特殊形式. 该文分别讨论了Minty向量似变分不等式的解与径向递减函数, 与向量优化问题的最优解或有效解之间的关系问题, 以及Minty向量似变分不等式的解集的仿射性质. 这些定理推广了文献中Minty变分不等式的一些重要的已知结果.     

半准内凸函数及其广义变分不等式

研究拓扑向量空间半准内凸函数的特性,给出半内凸弧方向可微函数的最小值可由一类似变分不等式问题的解表示及一类广义变分不等式解的唯一性。     

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．     

一类拟变分不等式的优化问题

本文用构造的方法证明了一类受拟变分不等式支配的优化问题的解的存在性。它提供在此基础上构造近似解的方法。     

关于非线性不等式组Levenberg-Marquardt算法的收敛性(英文)

本文研究了一类非线性不等式组的求解问题.利用一列目标函数两次可微的参数优化问题来逼近非线性不等式组的解,光滑Levenberg-Marquardt方法来求解参数优化问题,在一些较弱的条件下证明了文中算法的全局收敛性,数值实例显示文中算法效果较好.     

自反Banach空间内混合非线性似变分不等式解的算法*

本文在自反Banach空间内研究了一类混合非线性似变分不等式应用作者得到的一个极小极大不等式,对这类混合非线性似变分不等式的解,证明了几个存在唯一性定理其次由应用辅助问题技巧,作者建议了一个计算此类混合非线性似变分不等式的近似解的创新算法最后讨论收敛性准则.     

摩擦约束弹性力学广义变分不等式解的存在性和唯一性

阐述了等价于摩擦约束弹性力学基本问题的广义变分不等式问题解的存在性和唯一性,进而提出广义变分不等式有限元近似及其离散解法。     

集值或单值增算子变分不等式的有限维近似

将非线性变分不等式的有限维近似理论用来处理带集值增算子的变分不等式,得到近似解的存在性及收敛性定理。对一种特殊的单值情形,给出了收敛性理论及误差估计。     

一类广义凸多目标规划问题研究

首先引入了涉及高阶强Pre-invex函数的多目标优化问题m阶严格局部极小元的定义,在此基础上讨论了多目标优化问题的优化条件,最后研究了变分不等式的解与多目标优化问题高阶严格极小元之间的关系,其变分不等式的解正是多目标优化问题的高阶严格极小元,这些研究内容推广了Guneer-Bhatia给出的相关结论.     

参变极值问题的信息凝聚分布与Boltzmann极大熵函数

该文利用Ｂｏｌｔｚｍａｎｎ 熵概念给出了参变极值问题最优解的一种积分极限表达式和极值函数的极大熵函数,讨论了它们一致收敛性的要求并给出了极大熵函数一致收敛的一个充分条件,将之应用到全局最优解问题得到了全局最优解和最优值的一种显表示,最后还探讨了极大熵函数在一类双层规划问题求解中的应用．     

Gap Functions and Existence of Solutions to Set-Valued Vector Variational Inequalities

The variational inequality problem with set-valued mappings is very useful in economics and nonsmooth optimization. In this paper, we study the existence of solutions and the formulation of solution methods for vector variational inequalities (VVI) with set-valued mappings. We introduce gap functions and establish necessary and sufficient conditions for the existence of a solution of the VVI. It is shown that the optimization problem formulated by using gap functions can be transformed into a semi-infinite programming problem. We investigate also the existence of a solution for the generalized VVI with a set-valued mapping by virtue of the existence of a solution of the VVI with a single-valued function and a continuous selection theorem.     

On a Smooth Dual Gap Function for a Class of Quasi-Variational Inequalities

A well-known technique for the solution of quasi-variational inequalities (QVIs) consists in the reformulation of this problem as a constrained or unconstrained optimization problem by means of so-called gap functions. In contrast to standard variational inequalities, however, these gap functions turn out to be nonsmooth in general. Here, it is shown that one can obtain an unconstrained optimization reformulation of a class of QVIs with affine operator by using a continuously differentiable dual gap function. This extends an idea from Dietrich (J. Math. Anal. Appl. 235:380–393 [24]). Some numerical results illustrate the practical behavior of this dual gap function approach.     

On the Gap Functions of Prevariational Inequalities

Gap functions play a crucial role in transforming a variational inequality problem into an optimization problem. Then, methods solving an optimization problem can be exploited for finding a solution of a variational inequality problem. It is known that the so-called prevariational inequality is closely related to some generalized convex functions, such as linear fractional functions. In this paper, gap functions for several kinds of prevariational inequalities are investigated. More specifically, prevariational inequalities, extended prevariational inequalities, and extended weak vector prevariational inequalities are considered. Furthermore, a class of gap functions for inequality constrained prevariational inequalities is investigated via a nonlinear Lagrangian.     

求解变分不等式问题的一类新算法

变分不等式问题(简称VIP)通过广义D-gap函数可以转化成无约束优化问题.在找到使优化问题目标函数达到最大的y值后,直接构造了一类下降方向,使算法避免了求解梯度问题.最后证明了这种算法具有全局收敛性.     

Entropic perturbation method for solving a system of linear inequalities

The problem of finding an $\text{x∈}\text{R}{}^{\mathrm{n}}$ such that Ax⩽b and x⩾0 arises in numerous contexts. We propose a new optimization method for solving this feasibility problem. After converting Ax⩽b into a system of equations by introducing a slack variable for each of the linear inequalities, the method imposes an entropy function over both the original and the slack variables as the objective function. The resulting entropy optimization problem is convex and has an unconstrained convex dual. If the system is consistent and has an interior solution, then a closed-form formula converts the dual optimal solution to the primal optimal solution, which is a feasible solution for the original system of linear inequalities. An algorithm based on the Newton method is proposed for solving the unconstrained dual problem. The proposed algorithm enjoys the global convergence property with a quadratic rate of local convergence. However, if the system is inconsistent, the unconstrained dual is shown to be unbounded. Moreover, the same algorithm can detect possible inconsistency of the system. Our numerical examples reveal the insensitivity of the number of iterations to both the size of the problem and the distance between the initial solution and the feasible region. The performance of the proposed algorithm is compared to that of the surrogate constraint algorithm recently developed by Yang and Murty. Our comparison indicates that the proposed method is particularly suitable when the number of constraints is larger than that of the variables and the initial solution is not close to the feasible region.     

Gap Functions for Equilibrium Problems

The theory of gap functions, developed in the literature for variational inequalities, is extended to a general equilibrium problem. Descent methods, with exact an inexact line-search rules, are proposed. It is shown that these methods are a generalization of the gap function algorithms for variational inequalities and optimization problems.     

Convex optimization problems with arbitrary right-hand side perturbations

The problem of finding a solution to a system of mixed variational inequalities, which can be interpreted as a generalization of a primal–dual formulation of an optimization problem under arbitrary right-hand side perturbations, is considered. A number of various equilibrium type problems are particular cases of this problem. We suggest the problem to be reduced to a class of variational inequalities and propose a general descent type method to find its solution. If the primal cost function does not possess strengthened convexity properties, this descent method can be combined with a partial regularization method.     

Tabu Machine: A New Neural Network Solution Approach for Combinatorial Optimization Problems

A new artificial neural network solution approach is proposed to solve combinatorial optimization problems. The artificial neural network is called the Tabu Machine because it has the same structure as the Boltzmann Machine does but uses tabu search to govern its state transition mechanism. Similar to the Boltzmann Machine, the Tabu Machine consists of a set of binary state nodes connected with bidirectional arcs. Ruled by the transition mechanism, the nodes adjust their states in order to search for a global minimum energy state. Two combinatorial optimization problems, the maximum cut problem and the independent set problem, are used as examples to conduct a computational experiment. Without using overly sophisticated tabu search techniques, the Tabu Machine outperforms the Boltzmann Machine in terms of both solution quality and computation time.