基于动态优化双钻头打孔机生产效能的研究

首先,对孔型加工及走刀方式进行分析,为提高打孔机效能,采用两钻头同时作业的方式。然后,根据相关的数据,应用动态优化的相关理论建立数学模型,并运用遗传算法进行求解,从而确立了双钻头作业时的最优作业线路、最佳行进时间和作业成本,并用MATLAB编写程序画出了相应图形,更加形象地显现出了各个路段的最优作业方案。     

解决一类优化问题的变分迭代法

考虑利用变分迭代方法求解最优化问题,分别给出了求解一般的有约束和无约束最优化问题的基本步骤,并通过实例说明了所给方法的有效性.     

优化教学过程培养创新意识

培养具有创新精神和实践能力的高级专门人才是时代赋予高等教育的历史重任，而教学过程是实现这一目标的重要途径，文章就优化教学过程，在良好的教学过程中培养学生的创新意识作了一些探索。提出了相关的观点。     

出口商品结构优化的多元统计分析法及应用

探讨了出口商品结构优化的多元统计分析法及应用,并通过示例说明了该方法在出口商品结构优化中的应用。     

体操课教学训练过程最优化的探索

本文作者应用教学教育过程最优化的理论与方法，指导体操教学训练。经过多年的探索，实践证明要提高教学训练的效益和质量，就应发挥教学系统的整体功能，选择最佳教学方法，突出教学重点，抓住关键环节。     

一类资源分配问题的最优化方法的研究

建立了树型生产系统的一类资源分配问题的数学模型和计算法,并给出一般网络简化为树型网络的树化变换。然后建立了一般生产系统资源分配问题的网络优化算法。     

k-树的补图的最小填充和树宽

一个图的最小填充问题是寻求边数最少的弦母图,一个图的树宽问题是寻求团数最小的弦母图,这两个问题分别在稀疏矩阵计算及图的算法设计中有非常重要的作用．一个k-树G的补图G称为k-补树．本文给出了k-补树G的最小填充数f(G) 及树宽TW(G)．     

Method of lines with boundary elements for 1‐D transient diffusion‐reaction problems

Time‐dependent differential equations can be solved using the concept of method of lines (MOL) together with the boundary element (BE) representation for the spatial linear part of the equation. The BE method alleviates the need for spatial discretization and casts the problem in an integral format. Hence errors associated with the numerical approximation of the spatial derivatives are totally eliminated. An element level local cubic approximation is used for the variable at each time step to facilitate the time marching and the nonlinear terms are represented in a semi‐implicit manner by a local linearization at each time step. The accuracy of the method has been illustrated on a number of test problems of engineering significance. © 2005 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 2006     

Solving Problems with Min-Type Functions by Disjunctive Programming

We consider applications of disjunctive programming to global optimization and problems with equilibrium constraints. We propose a modification of the algorithm of F. Beaumont for disjunctive programming problems and show its numerical efficiency.     

A stochastic algorithm using one sample point per iteration and diminishing stepsizes

A stochastic algorithm for finding stationary points of real-valued functions defined on a Euclidean space is analyzed. It is based on the Robbins-Monro stochastic approximation procedure. Gradient evaluations are done by means of Monte Carlo simulations. At each iteratex _i , one sample point is drawn from an underlying probability space, based on which the gradient is approximated. The descent direction is against the approximation of the gradient, and the stepsize is 1/i. It is shown that, under broad conditions, w.p.1 if the sequence of iteratesx ₁,x ₂,...generated by the algorithm is bounded, then all of its accumulation points are stationary.