Irn(n=2-25)团簇基态结构的遗传算法研究

用遗传算法结合Gupta紧束缚模型势研究了Irn(n=2-25)团簇的基态结构.分析了Irn(n=2-25)团簇的基态结构随团簇尺寸的变化规律.计算结果表明,Irn(n=2-25)团簇的每个原子的平均束缚能和平均第一近邻随团簇尺寸的增加而增大,以总束缚能的二阶差分为判据,Irn(n=2-25)团簇的幻数是4、7、9、13、15、19、23.     

关于由衍射片的背面反射光形成的衍射图样的讨论

在光的衍射实验中,我们看到的一般是由通过衍射片的直射光形成的衍射图样,但在衍射片的另一面,同样存在着另一个衍射图样,它是由衍射片的背面反射光形成的,两个衍射图样关于衍射片对称.本文给出了这种实验现象,并用巴比涅原理解释了这种现象.     

多目标半定规划的加权中心路径法

对于线性型多目标半定规划问题，引进加权中心路径的概念，并利用单目标半定规划的中心路径法，提出了求解多目标半定规划问题的加权中心路径法，先得型对一个叔向量的有效解，然后在此基础上，提出了通过一次迭代得到对应一定范围内其他任意权向量的有效解的一步修正方法．     

Pushing Squares Around

We study dynamic self-reconfiguration of modular metamorphic systems. We guarantee the feasibility of motion planning in a rectangular model consisting of square modules that are allowed to slide along or rotate about one another. That is, we show that any two connected configurations of the same number of modules can be transformed into each other by a sequence of moves so that all intermediate configurations are connected. This settles a conjecture formulated in [6].     

模拟退火法在吸收薄膜的椭偏反演算法中的应用

将一种广泛用于求解复杂系统优化问题的技术－－模拟退火法－－用来求解椭偏反演方程。首先假设一个薄膜模型，计算出其相应的椭偏参数（Ψ，Δ）的值，在这个计算值的基础上加入不同标准偏差的高斯噪声；然后将加入噪声后的值（Ψm,Δm)作为模拟的测量数据，采用模拟退火算法进行求解，验证得知这种方法求得的薄膜参数很接近于假设的薄膜模型参数的真值，与其他文献的报道结果一致，而且在扩大搜寻范围时，仍然可以得到准确解，从而证明了该方法的可行性以及有效性。     

Feature Selection using Fuzzy Support Vector Machines

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machines and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machines have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach using fuzzy support vector machines and compare it with the previous work, the results of experiments on the UCI data sets show that feature selection using fuzzy SVM obtains better results than using SVM.     

带ARMA(1,1)条件异方差相关的随机波动模型的MCMC算法

我们首先提出了一个带ARMA(1,1)条件异方差相关的随机波动模型,它是基本的随机波动模型的一个自然的推广.进一步,对于这一新模型,我们给出了一个马尔可夫链蒙特卡罗(M CM C)算法.最后,利用该模型的模拟数据,展示了M CM C算法在这种模型中的应用.     

Approximating the single source unsplittable min-cost flow problem

In the single source unsplittable min-cost flow problem, commodities must be routed simultaneously from a common source vertex to certain destination vertices in a given graph with edge capacities and costs; the demand of each commodity must be routed along a single path so that the total flow through any edge is at most its capacity. Moreover, the total cost must not exceed a given budget. This problem has been introduced by Kleinberg [7] and generalizes several NP-complete problems from various areas in combinatorial optimization such as packing, partitioning, scheduling, load balancing, and virtual-circuit routing. Kolliopoulos and Stein [9] and Dinitz, Garg, and Goemans [4] developed algorithms improving the first approximation results of Kleinberg for the problem of minimizing the violation of edge capacities and for other variants. However, known techniques do not seem to be capable of providing solutions without also violating the cost constraint. We give the first approximation results with hard cost constraints. Moreover, all our results dominate the best known bicriteria approximations. Finally, we provide results on the hardness of approximation for several variants of the problem. Received: August 23, 2000 / Accepted: April 20, 2001?Published online October 2, 2001     

Pure adaptive search in monte carlo optimization

Pure adaptive search constructs a sequence of points uniformly distributed within a corresponding sequence of nested regions of the feasible space. At any stage, the next point in the sequence is chosen uniformly distributed over the region of feasible space containing all points that are equal or superior in value to the previous points in the sequence. We show that for convex programs the number of iterations required to achieve a given accuracy of solution increases at most linearly in the dimension of the problem. This compares to exponential growth in iterations required for pure random search.     

Analysis of Static Simulated Annealing Algorithms

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S²A), is analyzed using this measure. S²A uses a fixed cooling schedule during the algorithm execution. Though S²A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper.