对两种估计单属性关系大小方法的可信度讨论

估计两个单属性关系在连接属性上取相同值的个数的方法有两种:静态估计法和动态估计法.本文利用不精确性推理模型对这两种方法的可信度定量地进行讨论,并用具体例子进行说明.     

求Ramsey数最优下界值的递归算法

要确定每个具体的Ramsey数的数值是相当困难的,至今人们只求出了为数很少的几个Ramsey数的数值.人们在研究Ramsey数性质的同时,也在估计Ramsey数的数值,得出了某些Ramsey数的下界值,但工作进展缓慢.本文提出了一种计算Ramsey数最优下界值的递归算法,该算法利用当今关于Ramsey数的最新结果,能得出Ramsey数的目前最优下界值.1 算法描述不妨将本算法定名为G,参数个数为1个以上(可变化),算法允许递归调用,其输出值为Ramsey数的目前最优下界值.C(k_1,k_2…,k_n)表示以k_1,k_2…,k_n作为输入,通过算法G所得到的输出结果,即C(k_1,k_2…,k_n)表示的是G算出的Ramsey数N(k_1,k_2,…,k_n;2)的目前最优下界值,其中N(k_1,k_2…,k_n;2)的含意与文献[2]中有关含意相同.算法G:     

过程控制中计算机图形信息压缩存贮和图形滚动方法

对工业生产过程控制和管理中的计算机图形显示,提出一种压缩存贮和滚动的新方法。新方法的实施,可使图形数据占用的存贮空间减少约80％,一般地不需使用外存,从而大大提高了显示速度,解决了普通微型机存贮空间和显示速度之间的矛盾,有效地提高了普通微机的功能/价格比。所提出的方法均经工程实践检验过,效果良好。     

Introduction to Ant Colony Algorithm and Its Application in CIMS

Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics...     

互补问题中Gauss-Newton方法全局收敛性的推广(英文)

研究了由Subramamian为求解互补问题提出的阻尼Gauss-Newton方法的收敛性质，在较弱的条件下，给出了一个全局收敛效果，这个结果是Subramanian PK (1993)和（1997）中相应结果的一个推广。     

利用二次贝齐尔函数的一维优化算法

本文利用贝齐尔函数良好的保型性质,将它作为拟合函数,构造了一个一维优化算法。数值实验表明,对于高度非线性的目标函数,新算法具有很强的适应性,比同类算法更有效。     

硬盘分区链表中结点信息的完全恢复算法

通过对分区链表中结点信息的研究 ,及前后结点中相关数据项比较之后 ,参照磁盘基数表中所记录的相应分区长度 ,给出了其中任一结点或整个分区链表丢失之后的修复算法     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.     

A floating-point genetic algorithm for solving the unit commitment problem

This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding–decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of genetic algorithms, which can be applied to the practical scheduling tasks in utility power systems.     

关于可达矩阵的求法探讨

在《离散数学》、《图论》课程中 ,用矩阵表示图时 ,涉及到一类重要的矩阵——可达矩阵 ,它是判别图中任意两点是否有通路的重要手段 ,也是求强分图的重要方法 ,但是可达矩阵的求法比较复杂 .本文针对这一问题 ,对可达矩阵的求法进行了改进 ,提出了一种简单可行的算法 .