随着教育信息化的普及,教学方式和学习方法灵活多样,网络学习资源更是日益丰富.以学生在微积分课堂上对教师依赖性很强的现状入手,采用"问题导向+线上优质教学资源自主学习+线下课堂教学+线上线下答疑解惑"的混合模式,以课前问题为导向,通过线上线下自主学习体现"学生为主、教师为辅"的新观念,探索如何应用线上线下相结合的混合教学方式引导和帮助学生,从而改善学生对微积分的畏难心理,使提升学生的微积分成绩.

本文通过分析一个二阶微分方程的两个特解之间的相等关系,从而计算了Dirichlet积分.

Considering the problem QP is that minimum quadratic objective function with linear constraints.Under the hypothesis of the feasible region is non-empty compact set,using the KKT conditions of the problem,to bring the original problem is transformed into the problem of linear objective function with complementarity's constraints LPC.On a global optimization algorithm is proposed.The main idea of the method is to generate a sequence of points either ending at a global optimal solution within a finite number of iterations or converging to a global optimal solution of the LPC.To prove the finite convergence of the algorithm and by solving construction example is given to illustrate the effectiveness of this method.

结合我院近几年来针对经管类专业学生所开展的数学建模教育的实践，分析了经管类院校本科各专业的数学教育现状和大学生数学素质培养方面存在的一些问题，提出在经管类院校本科阶段数学建模教育的六个板块及其基本教学内容和实践环节。

最大团问题(MCP)是图论中的一个传统问题,在很多领域都有广泛的应用.主要利用已有研究的相关结论,将(MCP)的二次0-1规划模型等价转化为一个线性混合整数规划模型,再利用计算线性混合整数规划的软件求解.通过对所构造实例的计算,验证了求解(MCP)方法的有效性.

讨论了二次背包问题(QKP)的一种线性化方法.利用文献中的相关结论,通过增加变量和线性约束,将(QKP)的二次0-1规划模型等价转化为一个线性混合整数规划模型,再利用计算线性混合整数规划的软件(如Ilog-cplex或Lingo)求解,从而解决原问题.对所构造问题实例的计算,验证了求解(QKP)方法的有效性.

The generic algorithm and particle swarm optimization algorithm are compared by several benchmark functions.The result indicates that the latter is better in optimal value and searching speed than the former.

A class of constrained optimization problem with quadratic objective function and box constraints is studied.Using of a new approach which makes use of a global sub-differential (L-sub-differential),and explicit descriptions for quadratic functions,a sufficient condition to characterize a local optimal solution to be global is established.

In this paper,a class of constrained optimization problem with quadratic objective function and equality constraints is studied.We present a new approach which makes use of a global subdifferential—L-subdifferential(unlike convex subdifferential,an L-subdifferential is defined by functions which are not necessarity linear functions).It gives explicit descriptions to quadratic functions.Established is a sufficient condition which ensures that a feasible point is a global minimzer of a non-convex quadratic minimization problem subject to quadratic equality constraints.