多目标运输问题的Fnzzy线性规划解法

经典运输问题是一类特殊的单目标线性规划问题，可用表上作业法或单纯形法求其最优解［１］．近年来，许多学者研究了多目标运输问题，提出了相应的求解算法［６－９］．本文应用Ｆｕｚｚｙ线性规划的方法，给出了多目标运输问题的又一求解算法．     

修正的LMS方法

ＢｉｔｍｅａｄＲ．Ｒ和ＡｎｄｅｒｓｏｎＤ．Ｏ在文献［１］中为任意线性方程组的求解提出了一种颇为有效的算法，称为ＬＭＳ方法．文献［２］详细地论述了算法的收敛性，指出收敛极限是方程组的最小二乘解．本文为使解线性方程组的ＬＭＳ算法具有更广泛、更方便的应用性．对文献［２］中的ＬＭＳ算法作了修正．理论和实践证明修正后的算法是成功的．     

多目标规划αk-较多有效解类的若干性质

在［１］中,作者提出多目标规划的较多有效解和较多最优解概念,并研究了它们的基本性质．文［３］则讨论了ｋ－较多最优解的若干性质．文［４］利用较多序类进一步引进多目标规划问题的αｋ－较多有效解,并证明了这类解的最优性必要条件．本文再给出多目标规划问题的αｋ－较多最优解的概念,并讨论了多目标规划αｋ－较多有效解和αｋ－较多最优解的若干重要性质．     

对流-扩散问题的特征──块中心差分法

１．引言１９８２年,Ｄｏｕｇｌａｓ和Ｒｕｓｓｅｌｌ［１］提出解对流一扩散问题的特征一差分方法,网格节点为均匀分布,求解区域为直线Ｒ．文中讨论了基于二次插值的特征一差分格式,但其近似解按离散Ｌ２模未达到最优阶误差估计．１９８８年Ｗｅｉｓｅｒ和Ｗｈｅｅｌｅｒ［２］提出解线性椭圆型和线性抛物型方程的块中心差分法,１９９１年王申林［３］讨论了解拟线性双曲型积分微分方程的块中心差分方法,其共同特点为近似解按离散的Ｌ２模达到最优阶误差估计,解的一阶导数的近似解达到超收敛误差估计．１９９３年由同顺［４］讨论了…     

数值天气模式参数选择的全局优化方法

１引言数值天气预报模式中关于参数的选择直接影响到天气预报的准确率,在建立一个数值天气预报系统时,为了得到好的预报效果,必须对模式参数进行优化．在这方面已有许多文献［１］－［７］作过有益的探讨,提供了许多有效的方法,在文献［２］中,给出了一种参数反演的方法．并应用广义线性反演,获得较稳定的计算格式．然而,此方法在每一次迭代时,至少需要解ｎ＋１个正问题（其中ｎ为参数的个数）．又在文献［６］中。引进了四维同化的共轭梯度法,适宜于求解高维问题．然而,共轭梯度法只能求得局部最优解,对初始参数的选取很敏感,…     

对方案聚合方法的两点改进

１引言Ｒｏｃｋａｆｅｌｌａｒ和Ｗｅｔｓ共同提出的方案聚合方法（ＳｃｅｎａｒｉｏＡｇｇｒｅｇａｔｉｏｎＭｅｔｈｏｄ,简称ＳＡＭ）［１］已被公认为是解决随机规划的行之有效的且具有极大潜力的好方法．这种方法与以前的逼近方法完全不同,它将含有多阶段条件期望值的复杂的优化问题分解为一系列相对简单的确定性的平行的子问题,把子问题的最优解“聚合”起来做为原问题最优解的估计值．许多计算结果表明［２］［３］,这种方法是独特的、可行的．然而正如两位杰出的作者在［１］中指出的,这种方法还有很大的改进余地,这也正是ＳＡ…     

对LP问题有无穷多最优解判别定理证明的补充

本文指出了文献［１］中关于“无穷多最优解判别定理”证明中的不足，并给出了完整的证明     

关于一类自由作业机器排序问题

文章研究文［１］中提出的加工时间依赖于机器的自由作业排序问题。Ｍ．Ｄｏｒｏｒ在［１］中提出了一个算法（算法３．４）。最近，Ａ．Ｊ．Ｖａｋｈａｒｉａ、Ｂ．Ｃａｔａｙ［２］及项思明、唐国春［３］均指出Ｍ．Ｄｏｒｏｒ的算法不是最优的。项思明和唐国春提出对这类问题在机器连续加工情形下的一种求解方法，即将排序问题化成指派问题。本文对这种解法作了简化，并回答文［３］中提出的几个问题。     

解含非线线源项的变分不等式的加性广义Schwartz算法

１引言近年来,变分不等式区域分解算法取得了许多成果,就线性算子变分不等式而言,读者可参见［１］、［２］、［３］等参考文献中关于重叠型的Ｓｃｈｗａｒｔｚ算法的分析以及文献［４］中关于非重叠型的Ｓｃｈｗａｒｔｚ算法的讨论,就非线性算子变分不等式而言,读者可参见文献〔５〕、［６］、［７］中的有关结果．最近,在文献［８］中针对线性变分不等式问题,提出了一种加性广义Ｓｃｈｗａｒｔｚ算法,其数值算例表明,这种算法可通过调节参数从而使算法的收敛速度大大加快,较经典的加性和乘性Ｓｃｈｗａｒｔｚ算法,具有明显的数…     

热弹性理论准静态问题解的一般公式及其应用

本文利用分解定理［１］求得热弹性理论的准静态问题在无限空间中的Ｇｒｅｅｎ函数组．从而，借助于文［２］中的互易定理建立了该问题解的一般公式．     

Discretization methods for the solution of semi-infinite programming problems

In the first part of this paper, we prove the convergence of a class of discretization methods for the solution of nonlinear semi-infinite programming problems, which includes known methods for linear problems as special cases. In the second part, we modify and study this type of algorithms for linear problems and suggest a specific method which requires the solution of a quadratic programming problem at each iteration. With this algorithm, satisfactory results can also be obtained for a number of singular problems. We demonstrate the performance of the algorithm by several numerical examples of multivariate Chebyshev approximation problems.     

单侧接触问题的拟有效集方法

单侧接触问题可以模型化为一个带不等式约束的数学规划问题。针对不等式约束问题求解的困难,提出了一个拟有效集方法。在每次迭代中,先利用上次迭代得到的解将问题转化为一个无接触问题,然后以其解作为当前迭代的初始解,且在每次迭代里可以同时更换一组接触点对,而不是象Lemke方法那样每次迭代仅更换一个接触点对。因而,该算法极大地提高了求解效率,算例表明了该算法的高效性和可靠性。     

A finite algorithm for the least two-norm solution of a linear program 1

By perturbing properly a linear program to a separable quadratic program it is possible to solve the latter in its dual variable space by iterative techniques such as sparsity-preserving SOR (successive overtaxation techniques). In this way large sparse linear programs can be handled.

In this paper we give a new computational criterion to check whether the solution of the perturbed quadratic program provides the least 2-norm solution of the original linear program. This criterion improves on the criterion proposed in an earlier paper.

We also describe an algorithm for solving linear programs which is based on the SOR methods. The main property of this algorithm is that, under mild assumptions, it finds the least 2-norm solution of a linear program in a finite number of iteration.s     

一类新的罚函数与罚算法(英)

在本文中,我们提出了带不等式约束的非线性规划问题的一类新的罚函数,它的一个子类可以光滑逼近$l_1$罚函数. 基于此类新的罚函数我们给出了一种罚算法,这个算法的特点是每次迭代求出罚函数的全局精确解或非精确解. 在很弱的条件下算法总是可行的. 我们在不需要任何约束规范的情况下,证明了算法的全局收敛性. 最后给出了数值实验.     

On the Existence of Fixed Points for Approximate Value Iteration and Temporal-Difference Learning

Approximate value iteration is a simple algorithm that combats the curse of dimensionality in dynamic programs by approximating iterates of the classical value iteration algorithm in a spirit reminiscent of statistical regression. Each iteration of this algorithm can be viewed as an application of a modified dynamic programming operator to the current iterate. The hope is that the iterates converge to a fixed point of this operator, which will then serve as a useful approximation of the optimal value function. In this paper, we show that, in general, the modified dynamic programming operator need not possess a fixed point; therefore, approximate value iteration should not be expected to converge. We then propose a variant of approximate value iteration for which the associated operator is guaranteed to possess at least one fixed point. This variant is motivated by studies of temporal-difference (TD) learning, and existence of fixed points implies here existence of stationary points for the ordinary differential equation approximated by a version of TD that incorporates exploration.     

线性规划问题的一种求解算法

本通过初等变换，并剔除常变量和零变量而对所给的线性规划问题进行预处理，得到的等价问题不仅易找初始可行基且初始可行解较优，易差别无可行解情形，而且可能使所含方程个数与变量个数减少，从而减少了求解问题的计算量和迭代次数。     

Parallel algorithms for large-scale linearly constrained minimization problem

In this paper, two PVD-type algorithms are proposed for solving inseparable linear constraint optimization. Instead of computing the residual gradient function, the new algorithm uses the reduced gradients to construct the PVD directions in parallel computation, which can greatly reduce the computation amount each iteration and is closer to practical applications for solve large-scale nonlinear programming. Moreover, based on an active set computed by the coordinate rotation at each iteration, a feasible descent direction can be easily obtained by the extended reduced gradient method. The direction is then used as the PVD direction and a new PVD algorithm is proposed for the general linearly constrained optimization. And the global convergence is also proved.     

Some fundamental issues of basic line search algorithm for linear programming problems

In this article we present the fundamental idea, concepts and theorems of a basic line search algorithm for solving linear programming problems which can be regarded as an extension of the simplex method. However, unlike the iteration of the simplex method from a basic point to an improved adjacent basic point via pivot operation, the basic line search algorithm, also by pivot operation, moves from a basic line which contains two basic feasible points to an improved basic line which also contains two basic feasible points whose objective values are no worse than that of the two basic feasible points on the previous basic line. The basic line search algorithm may skip some adjacent vertices so that it converges to an optimal solution faster than the simplex method. For example, for a 2-dimensional problem, the basic line search algorithm can find an optimal solution with only one iteration.     

求群体多目标决策问题的一种可行方向交互算法

对于群体多目标决策问题,决策者可以各自的关于目标之间的权衡比表达其偏爱信息并进行决策.当个体权衡比具有加性性质时可得群体权衡比.本文以此构造一种求解群体非线性规划问题的交互算法.迭代中基于求解决非线性规划的Topkis-Veinott方法构造可行方向.在一定的条件下,算法收敛于所讨论问题的群体满意解.     

Generalized fractional programming: Algorithms and numerical experimentation

Several algorithms to solve the generalized fractional program are summarized and compared numerically in the linear case. These algorithms are iterative procedures requiring the solution of a linear programming problem at each iteration in the linear case. The most efficient algorithm is obtained by marrying the Newton approach within the Dinkelbach approach for fractional programming.