一类二层凸规划的分解法

研究了一类二层凸规划和与之相应的凸规划问题的等价性．并讨论了这类凸规划的对偶性和鞍点问题,最后给出了求解这类二层凸规划的一个分解法．     

关于二层规划的逼近问题

下层问题以上层决策变量作为参数,而上层是以下层问题的最优值作为响应 的一类最优化问题——二层规划问题。我们给出了由一系列此类二层规划去逼近原二层规划的逼近法,得到了这种逼近的一些有趣的结果.     

A dual-relax penalty function approach for solving nonlinear bilevel programming with linear lower level problem

The penalty function method, presented many years ago, is an important numerical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty function approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach.     

关于二层规划最优解新定义的几点注解

文献[2]提出了二层规划解的新定义,同时指出新定义能够解决更广泛的线性二层规划问题,并且如果线性二层规划的约束域为非空紧集,那么线性二层规划问题就存在Pareto最优解.本文用两个线性二层规划的例子说明文献[2]得出的结论是不可靠的,同时还分析了两种定义下的线性二层规划诱导域之间的关系.     

CVaR准则下的双层报童问题模型研究

本文以供应商为领导层,零售商为从属层。基于CVaR(Conditional Value-at- Risk)准则,建立了两个双层报童问题模型.对于零售商,在兼顾其利润收益的同时,使用了CVaR风险计量方法对其风险进行了有效监控.然后根据模型中下层规划的特点及已有结论将双层规划模型转换成单层规划进行求解,数值计算结果表明模型是有意义的.     

一个广义既约梯度变位算法及收敛性

对于具有非线性等式约束且变量有界的非线性规划问题，提出了一个由三阶段组成的广度既约梯度变位算法，即线性近似、既约梯度求极小和可行变位阶段．同时我们证明了该算法所具有的收敛性．     

求解具有确定性线性不等式约束的有补偿随机规划问题的近似ε——容许方向法

1引言形如线性不等式确定性约束的有补偿随机规划问题     

多层线性规划问题的整体优化算法

本文研究了求解多层线性规划问题的整体优化算法,利用流动等值面技术,证明了算法的有限终止性,并给出实际例子验证了算法的有效性.     

An adaptive genetic algorithm for solving bilevel linear programming problem

Bilevel linear programming,which consists of the objective functions of the upper level and lower level,is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem.Of all the algorithms,the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming.In this paper,we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation.In addition,some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm.The performance of this proposed algorithm is illustrated by the examples from references.