混合学习法前向网络多属性储层参数预测

应用前向网络描述地震属性和储层参数间的非线性映射关系时，经典的误差反向传播算法存在收敛速度慢，易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术，具有较快的收敛速度和较强的全局搜寻能力；误差反向传播算法本质上是梯度下降算法，注重局部搜索。混合学习法为两种学习算法交替执行，首先以粒子群优化算法训练网络，当误差能量在规定的迭代次数内不再发生变化时，采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点，具有良好的应用前景。     

分支蚁群动态扰动算法求解TSP问题

蚁群优化算法是一种求解组合优化难题的强启发式算法,它利用正反馈和并行计算原理,具备很强的搜索能力。近年来,蚁群优化算法广泛应用于TSP问题的研究。本文提出分支蚁群动态扰动(DPBAC)算法,该算法主要从5个方面对基本蚁群算法做出改进:引入分支策略选取出发城市;改进状态转移规则;引入变异策略改进蚂蚁路径;改进信息素更新规则;引入条件动态扰动策略。实验表明,该算法可以有效改善基本蚁群算法搜索时间较长、容易陷入局部极小等缺点。     

基于遗传算法的概率准则组合证券模拟求解

针对概率准则意义下的组合证券投资模型,采用随机模拟技术和遗传算法相结合的思 想, 设计出求解算法, 并用Matlab 语言实现. 求解算法适用于证券收益率服从任意分布的情 况, 甚至不考虑证券收益率分布, 用实际数据进行模拟和优化. 实例证明, 该算法有很好的 收敛性及较高的计算效率.     

Estimation of Distribution Functions Using Data from Different Environments

Suppose that when a unit operatesin a certain environment, its lifetime has distribution G,and when the unit operates in another environment, its lifetimehas a different distribution, say F. Moreover, supposethe unit is operated for a certain period of time in the firstenvironment and is then transferred to the second environment.Thus we observe a censored lifetime in the first environmentand a failure time of a ``used' unit in the second environment.We propose an EM algorithm approach for obtaining a self-consistentestimator of F. Moreover, suppose using observations from both environments.The case where failure times are subject to right censoring isconsidered as well. We also establish the maximum likelihoodestimator of F. Moreover, suppose when the unit is repairable. Applicationand simulation studies are presented to illustrate the methodsderived.     

Optimal Augmentation of a 2-Vertex-Connected Multigraph to a k-Edge-Connected and 3-Vertex-Connected Multigraph

Given an undirected multigraph G = (V, E) and two positive integers and k, we consider the problem of augmenting G by the smallest number of new edges to obtain an -edge-connected and k-vertex-connected multigraph. In this paper, we show that the problem can be solved in Õ(mn²) time for any fixed and k = 3 if an input multigraph G is 2-vertex-connected, where n = |V| and m is the number of pairs of adjacent vertices in G.     

On the Complexity of Constructing Evolutionary Trees

In this paper we study a few important tree optimization problems with applications to computational biology. These problems ask for trees that are consistent with an as large part of the given data as possible. We show that the maximum homeomorphic agreement subtree problem cannot be approximated within a factor of , where N is the input size, for any 0 in polynomial time unless P = NP, even if all the given trees are of height 2. On the other hand, we present an O(N log N)-time heuristic for the restriction of this problem to instances with O(1) trees of height O(1) yielding solutions within a constant factor of the optimum. We prove that the maximum inferred consensus tree problem is NP-complete, and provide a simple, fast heuristic for it yielding solutions within one third of the optimum. We also present a more specialized polynomial-time heuristic for the maximum inferred local consensus tree problem.     

CAI函数图形发生器FGG的设计与实现

介绍在Ｗｉｎｄｏｗｓ环境下用ＢｏｒｌａｎｄＣ＋＋３．１开发的通用函数图形发生器的结构和功能，讨论了实现中的表达式求值算法和图形生成算法。ＦＧＧ的主要特点是，对输入的任意一元、二元函数表达式可直接生成对应的二维和三维图形。ＦＧＧ作为ＣＡＩ写作工具的支撑软件，可用于ＣＡＩ课件的开发及演示。     

高等教育最优投资双层规划模型研究

高等教育投资具有"双层"的特点,上层为省级主管部门,下层为高等学校。本文成功运用了双层规划模型,在不考虑高校自筹发展资金投入的情况下,建立了高等教育最优投资双层规划模型,研究了模型最优解的存在性,给出并证明了模型最优解的等价形式,设计了模型解的算法并进行了算法复杂性分析。通过求解模型,可以同时得到省级主管部门和高等学校的最优投资决策方案。文章最后还给出了考虑高校自筹发展资金的两种情况下建立投资模型和求得最优解的方法。     

On Combinatorial Approximation of Covering 0-1 Integer Programs and Partial Set Cover

The problems dealt with in this paper are generalizations of the set cover problem, min{cx | Ax b, x {0,1}ⁿ}, where c Q₊ⁿ, A {0,1}^{m × n}, b 1. The covering 0-1 integer program is the one, in this formulation, with arbitrary nonnegative entries of A and b, while the partial set cover problem requires only m–K constrains (or more) in Ax b to be satisfied when integer K is additionall specified. While many approximation algorithms have been recently developed for these problems and their special cases, using computationally rather expensive (albeit polynomial) LP-rounding (or SDP-rounding), we present a more efficient purely combinatorial algorithm and investigate its approximation capability for them. It will be shown that, when compared with the best performance known today and obtained by rounding methods, although its performance comes short in some special cases, it is at least equally good in general, extends for partial vertex cover, and improves for weighted multicover, partial set cover, and further generalizations.     

Batch-Processing Scheduling with Setup Times

The problem is to minimize the total weighted completion time on a single batch-processing machine with setup times. The machine can process a batch of at most B jobs at one time, and the processing time of a batch is given by the longest processing time among the jobs in the batch. The setup time of a batch is given by the largest setup time among the jobs in the batch. This batch-processing problem reduces to the ordinary uni-processor scheduling problem when B = 1. In this paper we focus on the extreme case of B = +, i.e. a batch can contain any number of jobs. We present in this paper a polynomial-time approximation algorithm for the problem with a performance guarantee of 2. We further show that a special case of the problem can be solved in polynomial time.