电视广告效果预测模型

本文通过引入电视广告影响力指数 ,利用主成分分析和多维偏好线性规划方法 ,结合广告理论 ,构建了电视广告效果的预测模型 ;对模型的基本原理和求解方法作了分析和讨论 ,并通过一真实的广告案例对模型进行了检验 .     

不确定风险偏好下的直觉模糊匹配决策方法

针对匹配信息为直觉模糊偏好关系的双边匹配问题,给出一种考虑主体风险偏好态度的决策方法.首先,定义了加性一致性直觉模糊偏好关系;然后,综合考虑主体给定的偏好关系以及风险偏好参数,建立并求解一种一致性偏差最小的非线性规划模型,从而获得最优排序向量;进而将其作为相应匹配对象的匹配度信息.在此基础上,构建最大化双方匹配度之和的多目标匹配模型,使用极大极小法转化为单目标线性规划模型,求解模型得到匹配结果;最后,通过一个算例表明所提方法的可行性与有效性.     

不完全信息下多目标决策的一种新方法

基于部分偏好信息（目标权重）,本提出了多目标决策的一种线性规划算法,该法避免了获取偏好信息的困难,在较少信息下,为决策提供更普遍,易操作且有效的方案排序结果。最后进行了算例分析。     

投资组合模型

本文建立了考虑交易费用情况下的市场资产组合投资模型,并采用偏好系数加权法对资产的预期收益和总风险进行评价,给出在不同偏好系数下的模型最优解,然后模型讨论了一般情况下的最优投资求解方法,给出定理,在总金额大于某一量值时,可化为线性规划求解。     

基于CAR-DEA方法的环境效率评价研究

现有环境效率评价的DEA方法没有考虑多维偏好约束问题,即不同决策单元对不同期望产出和不期望产出的偏好不同. 以地区为例,不同地区对GDP、废水和废气赋予的权重偏好各不相同. 在这种情况下,由于各决策单元的偏好约束不同,形成多维偏好约束集,在传统DEA模型中容易出现无可行解现象. 针对这一问题,基于CAR-DEA方法,结合保证域理论,提出一种解决多维偏好约束集问题的环境效率评价模型. 采用中国工业系统的环境效率评价实例对提出的方法进行了分析和说明.     

研究国际证券投资及其有效集的多目标线性规划方法

本文给出国际证券组合投资决策的多目标线性规划模型，以及求解有效国际证券组合的偏好系数加权法．在此基础上，应用线性多数规划技术研究有效国际证券组合集的几何特征，并给出相应结论和简单算例．     

三元区间数线性规划及其解法

在三元区间数理论的基础上,提出了三元区间数线性规划模型的一般形式.利用三元区间数的弱偏好序关系,定义了三元区间数线性规划的弱可行解和弱最优解,并将它的弱最优解转化为求三个一般线性规划模型的最优解,从而在理论上对区间数线性规划进行拓展.最后,通过数值例子对该理论及模型求解方法进行说明和验证.     

信息不对称下的超对策冲突分析反问题研究

在信息不对称且对手为信息劣势方的冲突中,对手对决策者的偏好信息容易存在误解,而正确分析对手误解的偏好将有利于决策者在冲突谈判中占据主动优势,并为后期诱导机制的设立提供决策支持。本文基于超对策理论,从冲突分析的反向视角出发,提出了求解对手误解偏好的具体方法,利用谈判中对手显露出的均衡信息,建立了求解对手误解偏好的0-1非线性规划模型。为便于求解,将该模型转化成等效的0-1线性规划模型,并进一步采用CB cuts方法及相应的算法求取该模型的所有最优解。通过双渠道冲突案例分析,进一步验证了该方法对于求解实际冲突中误解偏好的有效性。最后,根据求得的误解偏好概述了决策者如何选择诱导策略,以实现自己利益的最大化。     

投资组合与模糊规划模型

本文讨论了投资的风险与收益的问题,首先我们给出了一个比较完整的模型,然后,考虑投资数额相当大时的一个近似处理模型,并分别用偏好系数加权法和模糊线性规划法进行了求解,接下来,我们又考虑了如何处理投资额相对较小的情况下的最优投资组合情况,引入了绝对收益率进行了较为有效的解决。     

多维参数线性规划的解分割和方向导数

基于最优分割的概念,消费系数多维参数化的线性规划的最优值函数和临界域特征被研究.证明了最优值函数的有效域可分解为有限多个相对内部不交的临界域,这是不同于基于最优基概念所得结论.并给出一个求解任何点处任何方向导数的线性规划方法.     

基于不同线搜索技术的改进Zoutendijk可行方向法及Matlab实现——浅谈在交通配流问题上的应用

针对于求解带线性约束的非线性规划模型的Zoutendijk可行方向法在Matlab实现过程中的几个问题,提出了相应的改进方法及其Matlab实现,并将其应用于一类交通配流模型,得到了基于各种不同线搜索技术下的改进Zoutendijk可行方向法计算结果并进行了比较分析,发现非精确搜索下的改进Zoutendijk法更具有实用性.     

关于一个改进的既约梯度法的收敛速度

An improved reduced gradient method was proposed in [4] to solve the nonlinear programming (P) with linear constraints.     

Fixing variables and generating classical cutting planes when using an interior point branch and cut method to solve integer programming problems

Branch and cut methods for integer programming problems solve a sequence of linear programming problems. Traditionally, these linear programming relaxations have been solved using the simplex method. The reduced costs available at the optimal solution to a relaxation may make it possible to fix variables at zero or one. If the solution to a relaxation is fractional, additional constraints can be generated which cut off the solution to the relaxation, but donot cut off any feasible integer points. Gomory cutting planes and other classes of cutting planes are generated from the final tableau. In this paper, we consider using an interior point method to solve the linear programming relaxations. We show that it is still possible to generate Gomory cuts and other cuts without having to recreate a tableau, and we also show how variables can be fixed without using the optimal reduced costs. The procedures we develop do not require that the current relaxation be solved to optimality; this is useful for an interior point method because early termination of the current relaxation results in an improved starting point for the next relaxation.     

Improved logarithmic linearizing method for optimization problems with free-sign pure discrete signomial terms

Free-sign pure discrete signomial (FPDS) terms are vital to and are frequently observed in many nonlinear programming problems, such as geometric programming, generalized geometric programming, and mixed-integer non-linear programming problems. In this study, all variables in the FPDS term are discrete variables. Any improvement to techniques for linearizing FPDS term contributes significantly to the solving of nonlinear programming problems; therefore, relative techniques have continually been developed. This study develops an improved exact method to linearize a FPDS term into a set of linear programs with minimal logarithmic numbers of zero-one variables and constraints. This method is tighter than current methods. Various numerical experiments demonstrate that the proposed method is significantly more efficient than current methods, especially when the problem scale is large.     

A simplex algorithm for piecewise-linear fractional programming problems

Generalizations of the well-known simplex method for linear programming are available to solve the piecewise linear programming problem and the linear fractional programming problem. In this paper we consider a further generalization of the simplex method to solve piecewise linear fractional programming problems unifying the simplex method for linear programs, piecewise linear programs, and the linear fractional programs. Computational results are presented to obtain further insights into the behavior of the algorithm on random test problems.     

Note on “Some models for deriving the priority weights from interval fuzzy preference relations”

Deriving accurate interval weights from interval fuzzy preference relations is key to successfully solving decision making problems. Xu and Chen (2008) proposed a number of linear programming models to derive interval weights, but the definitions for the additive consistent interval fuzzy preference relation and the linear programming model still need to be improved. In this paper, a numerical example is given to show how these definitions and models can be improved to increase accuracy. A new additive consistency definition for interval fuzzy preference relations is proposed and novel linear programming models are established to demonstrate the generation of interval weights from an interval fuzzy preference relation.     

Optimization over structured subsets of positive semidefinite matrices via column generation

We develop algorithms to construct inner approximations of the cone of positive semidefinite matrices via linear programming and second order cone programming. Starting with an initial linear algebraic approximation suggested recently by Ahmadi and Majumdar, we describe an iterative process through which our approximation is improved at every step. This is done using ideas from column generation in large-scale linear programming. We then apply these techniques to approximate the sum of squares cone in a nonconvex polynomial optimization setting, and the copositive cone for a discrete optimization problem.     

关于二次规划问题分段线性同伦算法的改进

本文利用Ｃｈｏｌｅｓｋｙ分解，Ｇａｕｓｓ消去等技术和定义适当的同伦映射，将关于二次规划问题的分段线性同伦算法加以改进，改进后的算法，对于严格凸二次规划来说，计算效率与Ｇｏｌｄｆａｒｂ－Ｉｄｎａｎｉ的对偶法相当。     

半定规划的近似中心投影法

1．引言半定规划问题标准形的数学形式是这里C，AIEIR”””及变量XEIRn“”为对称矩阵，Tr（·）表示矩阵的迹，用符号＞0和三0分别表示矩阵正定和半正定．由于半定规划在控制论，结构优化，组合优化方面有重要应用［1，3，16，17］以及线性规划内点法取得的巨大成就［7］，将线性规划的内点法推广到半定规划上，是数学规划领域内近年来受到重视的一个研究课题．线性规划内点法中的势函数下降法［10，16］原始对偶中心路径跟踪法［2，4，8，9，11。15］已经先后被推广到半定规划上．ROOS－Visl近似中心法则是求解线性规划的另一类内…     

含直觉模糊弹性约束的广义模糊变量线性规划

本文基于模糊结构元方法建立并讨论了一类含有直觉模糊弹性约束的广义模糊变量线性 规划问题。首先,简单介绍了结构元方法并对结构元加权排序中权函数表征决策者风险态度进行了深入分析。然后,通过选取风险中立型决策态度来定义序关系并拓展Verdegay模糊线性规划方法,将新型模糊变量线性规划问题转化为两个含一般模糊弹性约束的模糊变量线性规划模型,给出了此类规划最优直觉模糊解的求法。最后,通过数值算例进一步说明该方法的有效性。