求线性规划问题初始可行基的一种方法

Smale 证明了采用单纯形法求解线性规划问题,在概率平均意义下转轴次数为变量数目的线性函数.下面介绍不引进人工变量,直接由所给问题的标准形式     

单纯形法的旋转迭代算法及影子价格

本文对线性规划问题提出一种寻找初始可行基和判定可行解的统一方法，它在运用单纯形法时，在若干情况下不必引入人工变量而可在一种表格之下直接应用旋转运算而获得，之后就在同一张表格下完全和常规单纯形法一样求最优解，此法我们称之为“单纯形法的旋转迭代算法”，应用此法，我们容易求出影子价格。     

含自由变量LP问题的改进单纯形法

对于含自由变量的LP问题,为了得到比单纯形法[1]更有效的算法,通过研究在单纯形法迭代过程中,将自由变量化为非负变量再实施运算的规律,提出一种能节省存贮空间和提高运算速度的改进单纯形法。数值实验表明新算法是有效的。     

求解线性规划的对偶算法

单纯形法一般采用行变换进行计算.本文给出了两种列变换的计算方法,一种与原始单纯形法等价,一种与对偶单纯形法等价,本文称之为对偶方法.这两种方法不引入松弛变量或剩余变量,计算规模小,有明显竞争优势.     

变量有广义界线性规划的直接对偶单纯形法

本文讨论变量有广义界线性规划问题借助标准形线性规划同单纯形法技术，建立问题的一个直接对偶单纯形法。分析了方法的性质，给出了初始对偶可行基的计算方法，并用实例说明方法的具体操作。     

可行下降视角下退化运输问题的最优性条件

运输问题是一类特殊的线性规划问题,通常用特殊的单纯形法—运输单纯形法(也叫表上作业法)进行求解,其最优性条件为所有非基变量的检验数大于等于零.针对实际算例中出现的某个非基变量的检验数小于零,却已经达到最优的情况,从可行下降方向的角度进行了探讨.结论表明:一般情况下非基变量的检验数大于等于零仅是运输问题最优解的充分条件;而问题非退化时,该判别条件成为充要条件.     

线性规划单纯形法的动态灵敏度分析及其应用

本文研究了线性规划的灵敏度分析方法.运用灵敏度分析的方法,分析了单纯形法求解过程中新增变量的动态变化所需的条件,并从具体的二维和三维例子出发,构造出一系列的高维线性规划问题.用单纯形法求解这些问题时,使用某种主元规则(如最大改进规则)的迭代次数可以比约束数目多一至三次.     

一种用于线性规划算法的换基准则

本文就线性规划中的对偶单纯形法和运输问题中的表上作业法选取出基变量或者对基变量的准则进行改进,从而得出一种新的换基准则.按该方法进行优化运算,可以使算法的迭代次数减到最少,从而加快了运算速度.     

线性规划初始基可行解的一种直接算法

本文通过引入可变动目标函数的预规划来求线性的初始基可行解，可以大量减少外额变量的个数，从而简化了二阶段单纯形法，使之便于在计算机上实现。     

关于变量有上限的线性规划问题的一种解法

对变量有上限的线性规划问题:中作了讨论,其算法由于不必扩大系数矩阵而被广泛采用,但伴随离基变量的六种选择,找初始解和换基都需增加运算量和程序,且不易掌握,无论讲授和学习起来都颇费时数。对问题(1)～(3)的另一种处理是[2]和[3]中介绍的对偶单纯形法,它是先置限制(3)于不顾,通过对问题(1)～(2)中每一正检验数λ_l作变换先得到正则解,继之用对偶单纯形法迭代求解。倘若求得的最优解中某变量x_9不满足上限约束(3),则仍作代换     

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.     

三层前向人工神经网络全局最优逼近

提出了求解不等式约束非线性优化问题的群体复合形进化算法 ,提出的算法能充分利用目标函数值的信息、优化搜索过程具有较强的方向性和目标性 ,收敛速度较快 ,且是全局优化算法 ;将群体复合形进化算法应用于三层前向人工神经网络逼近 ,提出了三层前向人工神经网络全局最优逼近算法 ;将三层前向人工神经网络全局最优逼近算法应用于实例 ,表明了提出的全局最优逼近算法的有效性 .     

避免引入人工变量求线性规划可行基的一个新方法

讨论了线性规划的单纯形解法,给出了不须加人工变量就可得到一个可行基的算法.通过大量的算例表明此法比传统的单纯形方法具有算法结构简单,计算量小的优点.     

基于人工蜂群算法的配送中心选址问题求解

采用人工蜂群算法对配送中心选址问题进行求解,给出食物源的编码方法,通过整数规范化,使算法能在整数空间内对问题进行求解.应用算法进行了仿真实验,并将结果与其它一些启发式算法进行了比较和分析.计算结果表明人工蜂群算法可以有效求解配送中心选址问题,同时也为算法求解其它一些组合优化问题提供了有益思路.     

Dirichlet-Neumann alternating algorithm for an exterior anisotropic quasilinear elliptic problem

In this paper, by the Kirchhoff transformation, a Dirichlet-Neumann (D-N) alternating algorithm which is a non-overlapping domain decomposition method based on natural boundary reduction is discussed for solving exterior anisotropic quasilinear problems with circular artificial boundary. By the principle of the natural boundary reduction, we obtain natural integral equation for the anisotropic quasilinear problems on circular artificial boundaries and construct the algorithm and analyze its convergence. Moreover, the convergence rate is obtained in detail for a typical domain. Finally, some numerical examples are presented to illustrate the feasibility of the method.     

Clustering of functional data in a low-dimensional subspace

To find optimal clusters of functional objects in a lower-dimensional subspace of data, a sequential method called tandem analysis, is often used, though such a method is problematic. A new procedure is developed to find optimal clusters of functional objects and also find an optimal subspace for clustering, simultaneously. The method is based on the k-means criterion for functional data and seeks the subspace that is maximally informative about the clustering structure in the data. An efficient alternating least-squares algorithm is described, and the proposed method is extended to a regularized method. Analyses of artificial and real data examples demonstrate that the proposed method gives correct and interpretable results.     

An overview on the simplex algorithm

In this paper, the simplex algorithm and its variants are investigated. First, we define a new concept called formal tableau, which leads to derive easily the dual solution from the latest primal table; without any distinction between the original variables and the slack ones. Second, we propose a new method for initializing the simplex algorithm. Unlike the two-phase and the big-M methods, our technique does not involve artificial variables. The computational results reveal that this new method is very favorable especially when the number of artificial variables is significant. Finally, this method will be combined with the notion of formal tableau leading naturally to a second new approach.     

A new model and a hyper-heuristic approach for two-dimensional shelf space allocation

In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function. We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithm with a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improved when compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.     

一类凸二次规划的新算法

本文给出了求解一类凸二次规划问题的新算法.这种算法既保留了传统算法的优点,又避免了其它算法中出现的添加人工变量过多、循环等问题.算例表明,这种算法是简便而有效的.     

Theorem of learning for a competition algorithm

This paper is an extension of [1], where a new decisive algorithm was proposed. In its operation, the unit resembles artificial neural networks. However, the functioning of the algorithm proposed is based on different concepts. It does not use the concept of a net or a neuron. The theorem of learning for the new competition algorithm is proved.