以功效函数为主线的假设检验教学

本文基于假设检验相关概念的统一,以及初等统计和高等统计学衔接的考虑,提出了以功效函数为主线的教学设计.首先基于证据原理确定拒绝域的形式,计算相应的功效函数;然后用功效函数描述假设检验的两类错误概率;最后根据给定的显著性水平运用功效函数确定拒绝域的具体形式.     

寻找具有三个任意函数的变系数KdV-MKdV方程的类孤波解的新方法

给出了求具有三个任意函数的变系数非线性演化方程的类孤波解的截断展开方法。这种方法的关键是首先把形式解设为几个待定函数的截断展开形式，从而可将变系数非线性演化方程转化为一组待定函数的代数方程，然后进一步给出容易积分的待定函数的常微分方程组，从而构造出相应的类孤波解。     

函数方程的高等数学解法

近年来,解函数方程引起了一些作者的兴趣。仅“数学通报”发表的文章就不下三、四篇。不过,就方法而言,它们都是初等的。其实,用微积分的方法或实变函数的手段来解此类方程,将会更有效和更一般些。当然,函数方程这一课题范围太广且复杂,即使是对具有某种确定结构的形式来说,也只能在一定条件或一定范     

基于粒子进化的SD多功效流率函数及其应用

系统动力学(SD)模型的流率是动态仿真决策控制要素,但在SD方法中,仅给出定义式,解析范式缺失,实际应用常采用模糊确定的表函数.由于SD模型的流位演进流率与粒子群算法(PSO)的粒子进化速度属性类同,则在析解流率决策控制机制与粒子进化速度方程的基础上,依据粒子进化的结构原理,讨论流率函数结构,建立多功效流率函数解析式,提出系数估计策略,构造动态仿真算法.以区域生态经济系统动态仿真为例,将流率函数及其系数估计策略和动态仿真算法加以实际应用,以检验多功效流率函数的有效性和适用性.多功效流率函数物理意义明确,具有可比可控性、结构优化性和减少主观性等多重功效,可作为SD方法中流率函数的范式.     

具有n阶转向点方程的渐近解的完全表达式

本文研究二阶线性常微分方程，使用广义Ａｉｒｙ函数得到了方程在转向点附近形式一致有效渐近解的完全表达式。     

Riccati展开法求非线性Konno-Oono耦合方程的新精确解

应用Riccati展开法,给出了非线性Konno-Oono方程的一系列新精确解.这些解的形式包括三角函数解、双曲函数解、有理函数解.最后,对特殊函数下的精确解进行数值模拟,给出这些精确解的直观表示.     

生长竞争型函数优化的蚁群算法

提出一种求解函数优化的生长竞争型蚁群算法.该方法利用植物生长的向光性机制,引入竞争机制,获取局部的最优解,再结合蚁群算法,实现全局优化.算法在MATLAB环境下,对一些典型的测试函数进行了求解和验证,实验结果表明该方法是一种求解函数优化的有效方法.     

广田双线性方法与(3+1)维Jimbo-Miwa-Like方程的几种新解

基于广田双线性方法,将(3+1)维Jimbo-Miwa-Like方程化为双线性形式,在此基础上,首先利用试探函数法与符号计算系统Mathematica,获得了方程的由指数函数、三角函数和双曲函数形式的单函数解;另外,获得了由三角函数、指数函数与双曲函数两两组合的复合型解.最后通过选取适当的参数,做出一部分精确解的图形来说明它们的性质.     

具有分数形式的随机目标规划在金属模型中的应用

本文主要应用了Enrique Ballestero提出的一个新的随机目标规划框架，采用了幂效用函数和双曲绝对风险厌恶，以资产组合选择问题为背景，构造了两个具有分数形式目标函数的随机目标规划模型，给出了解法，并讨论了解的经济意义。本文的随机目标规划产生了一种相对风险极小的有效解，为决策提供了一种新的方案选择途径。     

函数不连续的二阶拟线性奇摄动边值问题

讨论了函数不连续情况下二阶拟线性奇摄动边值问题,用边界层函数法和轨道的光滑缝接,构造了问题的形式渐近解,并在整个区间上证明了形式渐近解的一致有效性,把吉洪诺夫系统中的函数光滑条件推广到了不连续情况.     

Lower semi-continuity of the Pareto solution map in quasiconvex semi-infinite vector optimization

This paper is concerned with the lower semi-continuity of the efficient (Pareto) solution map for the perturbed quasiconvex semi-infinite vector optimization problem (QCSVO). We establish sufficient conditions for the lower semi-continuous property of the efficient solution map of (QCSVO) under functional perturbations of both the objective function and the constraints. Examples are designed to analyze the obtained results.     

多目标条件风险值的一种近似求解方法

本文研究了一种求解多目标条件风险值问题的近似方法,首先引入了多个损失函数在对应的置信水平下关于一个证券组合的α-VaR损失值,以及α-CVaR损失值概念．α-CVaR损失值表明了在给定的证券组合于置信水平对应的最小信用风险值的条件期望损失值,那么求出这样的最小条件期望损失值的模型构成了一个求解α-CVaR损失值的多目标问题,它的解就是最小条件期望损失值的有效证券组合,即Pareto弱有效解．为了求解它的Pareto弱有效解,我们引进了损失函数对应的优化问题(SCVaR),可以通过求解非线性规划问题(SCVaR)的最优解近似地刻画α—CVaR损失值,这样使得求解α-CVaR损失值变得容易．     

Stability and Scalarization of Weak Efficient, Efficient and Henig Proper Efficient Sets Using Generalized Quasiconvexities

The aim of this paper is to establish the stability of weak efficient, efficient and Henig proper efficient sets of a vector optimization problem, using quasiconvex and related functions. We establish the Kuratowski?CPainlevé set-convergence of the minimal solution sets of a family of perturbed problems to the corresponding minimal solution set of the vector problem, where the perturbations are performed on both the objective function and the feasible set. This convergence is established by using gamma convergence of the sequence of the perturbed objective functions and Kuratowski?CPainlevé set-convergence of the sequence of the perturbed feasible sets. The solution sets of the vector problem are characterized in terms of the solution sets of a scalar problem, where the scalarization function satisfies order preserving and order representing properties. This characterization is further used to establish the Kuratowski?CPainlevé set-convergence of the solution sets of a family of scalarized problems to the solution sets of the vector problem.     

Sufficient conditions for pseudo-Lipschitz property in convex semi-infinite vector optimization problems

This paper is devoted to the study of the pseudo-Lipschitz property of the efficient (Pareto) solution map for the perturbed convex semi-infinite vector optimization problem (CSVO). We establish sufficient conditions for the pseudo-Lipschitz property of the efficient solution map of (CSVO) under continuous perturbations of the right-hand side of the constraints and functional perturbations of the objective function. Examples are given to illustrate the obtained results.     

Integer Programming Duality in Multiple Objective Programming

The weighted sums approach for linear and convex multiple criteria optimization is well studied. The weights determine a linear function of the criteria approximating a decision makers overall utility. Any efficient solution may be found in this way. This is not the case for multiple criteria integer programming. However, in this case one may apply the more general e-constraint approach, resulting in particular single-criteria integer programming problems to generate efficient solutions. We show how this approach implies a more general, composite utility function of the criteria yielding a unified treatment of multiple criteria optimization with and without integrality constraints. Moreover, any efficient solution can be found using appropriate composite functions. The functions may be generated by the classical solution methods such as cutting plane and branch and bound algorithms.     

Bilinear optimization in the analysis of multicriteria problems using criteria importance theory under inexact information about preferences

Exact efficient numerical methods are proposed for solving bilinear optimization problems that arise when various solution variants are compared based on their preferability using an additive value function in the case of interval estimates of the degrees of superiority of certain criteria over others and in the case of interval restrictions on the growth of preferences along the criteria range.     

Global efficiency in multiobjective programming *

In this paper, we are concerned with global efficiency in multiobjective optimization. After exposing a property of a cone-subconvexlike function, we prove that a local weakly efficient solution, a local efficient solution and a local properly efficient solution are respectively a global weakly efficient solution, a global efficient solution and a global properly efficient solution of a multiobjective programming problem if cone- subconvexlikeness or cone-pre-invexity is assumed     

Maximal and supremal tolerances in multiobjective linear programming

Let a multiobjective linear programming problem and any efficient solution be given. Tolerance analysis aims to compute interval tolerances for (possibly all) objective function coefficients such that the efficient solution remains efficient for any perturbation of the coefficients within the computed intervals. The known methods either yield tolerances that are not the maximal possible ones, or they consider perturbations of weights of the weighted sum scalarization only. We focus directly on perturbations of the objective function coefficients, which makes the approach independent on a scalarization technique used. In this paper, we propose a method for calculating the supremal tolerance (the maximal one need not exist). The main disadvantage of the method is the exponential running time in the worst case. Nevertheless, we show that the problem of determining the maximal/supremal tolerance is NP-hard, so an efficient (polynomial time) procedure is not likely to exist. We illustrate our approach on examples and present an application in transportation problems. Since the maximal tolerance may be small, we extend the notion to individual lower and upper tolerances for each objective function coefficient. An algorithm for computing maximal individual tolerances is proposed.     

On Resource Allocation Problems with Interval-Scale Coefficients

In this paper we discuss the classical problem of the allocation of a single finite resource among many competing activities for the specific case where the coefficients of the objective function form an interval scale. In this case there is no longer a single optimal solution, but rather a set of efficient solutions. We recommend a technique equivalent to parametric objective function analysis to generate the set of efficient solutions to the problem.     

Convergence of the projected gradient method for quasiconvex multiobjective optimization

We consider the projected gradient method for solving the problem of finding a Pareto optimum of a quasiconvex multiobjective function. We show convergence of the sequence generated by the algorithm to a stationary point. Furthermore, when the components of the multiobjective function are pseudoconvex, we obtain that the generated sequence converges to a weakly efficient solution.