SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES AND COMPRESSION COSTS （PART I：EQUAL TIMES AND COSTS）

Most papers in scheduling research have treated individual job processing times as fixed parameters. However, in many practical situations, a manager may control processing time by realloeating resources. In this paper, authors consider a machine scheduling problemwith controllable processing times. In the first part of this paper, a special case where the pro-cessing times and compression costs are uniform among jobs is discussed. Theoretical results are derived that aid in developing an O（n^2) algorithm to slove the problem optimally. In the second part of this paper, authors generalize the discussion to general case, An effective heuristic to the genera/ problem will be presented.     

Affine scaling algorithm fails for semidefinite programming

In this paper, we introduce an affine scaling algorithm for semidefinite programming (SDP), and give an example of a semidefinite program such that the affine scaling algorithm converges to a non-optimal point. Both our program and its dual have interior feasible solutions and unique optimal solutions which satisfy strict complementarity, and they are non-degenerate everywhere.     

关系模式关键字的一个求解算法

本文给出了关系数据库模式中求解关键字的一个算法。算法很简洁并且对于[1]定义的一大类问题,执行时间是多项式级的。     

A decomposition heuristics for the container ship stowage problem

In this paper we face the problem of stowing a containership, referred to as the Master Bay Plan Problem (MBPP); this problem is difficult to solve due to its combinatorial nature and the constraints related to both the ship and the containers. We present a decomposition approach that allows us to assign a priori the bays of a containership to the set of containers to be loaded according to their final destination, such that different portions of the ship are independently considered for the stowage. Then, we find the optimal solution of each subset of bays by using a 0/1 Linear Programming model. Finally, we check the global ship stability of the overall stowage plan and look for its feasibility by using an exchange algorithm which is based on local search techniques. The validation of the proposed approach is performed with some real life test cases. This work has been developed within the research area: “The harbour as a logistic node” of the Italian Centre of Excellence on Integrated Logistics (CIELI) of the University of Genoa, Italy     

线性二次型最优控制的并行算法和软件

构造了线性二次型最优控制的并行算法，介绍了这个并行算法在武汉大学“ＷＵＤＰ９１”并行分布式处理系统上试算的数值应用软件的框图．本软件适用于既定动态系统的平衡问题．对于经济系统，可通过政策控制变量来调节和改善其状态和响应．对于自治系统可找出最优控制使得消耗函数达到最小值．通过对一系列例子进行试算，结果证实，所构造的并行算法和相应的数值软件是有效的，其加速比约为７．     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

10 Gb/s光纤通信系统一阶偏振模色散动态补偿系统反馈控制的实现

在偏振模色散(PMD)自动补偿技术中,如何根据反馈信号得到相应的控制信号,使补偿速度跟随偏振模色散变化始终是该技术的一个核心问题。提出了一种新颖的自适应抖动跟踪算法,完成了以微波信号为反馈的多自由度的一阶偏振模色散自动反馈补偿系统的跟踪补偿实验。算法成功地解决了传统算法在跟踪搜索过程中易陷入局部极值的问题,有效地克服了系统中的重要控制器件偏振控制器的磁滞现象以及动态补偿时跟踪搜索过程中易出现的瞬间恶化现象。实验结果表明该算法在对出现突发偏振模色散扰动后自动进行补偿的响应速度在ms量级,最快能达到1~2 ms。     

两两序列比对的一种新方法

本文研究DNA的两两序列比时,提出了基于快速沃尔什变换的新方法。经过计算模拟分析可知,比对的时间复杂度和空间复杂度明显降低.     

松弛型并行多分裂方法解非线性方程组的安全界

本文对某些非线性方程组F(x)=0,导出了一个算法,用它可以迭代建立F(x)=0的解的紧致上、下界。算法基于某些矩阵的多分裂,因此具有自然的并行性。我们证明了趋向于解的界之收敛原则,给出了参数的收敛性区域并考察了方法的收敛速度。     

A Monte Carlo method for an objective Bayesian procedure

This paper describes a method for an objective selection of the optimal prior distribution, or for adjusting its hyper-parameter, among the competing priors for a variety of Bayesian models. In order to implement this method, the integration of very high dimensional functions is required to get the normalizing constants of the posterior and even of the prior distribution. The logarithm of the high dimensional integral is reduced to the one-dimensional integration of a cerain function with respect to the scalar parameter over the range of the unit interval. Having decided the prior, the Bayes estimate or the posterior mean is used mainly here in addition to the posterior mode. All of these are based on the simulation of Gibbs distributions such as Metropolis' Monte Carlo algorithm. The improvement of the integration's accuracy is substantial in comparison with the conventional crude Monte Carlo integration. In the present method, we have essentially no practical restrictions in modeling the prior and the likelihood. Illustrative artificial data of the lattice system are given to show the practicability of the present procedure.