线性森林定常发展系统半离散化的研究

利用半离散的方法将线性森林发展方程中的μ(r)进行离散,得到两个偏微分方程,进一步利用算子半群的理论证明离散后的解是收敛于原方程的解.     

关于人口发展方程半离散算法的研究

利用半离散的方法将人口发展方程的边界条件进行离散,离散后得到两个相应的偏微分方程模型,然后利用算子半群的理论证明了离散后的解都逼近原方程的解,从而证明这种半离散方法是可行的.     

两相同部件冷贮备可修系统半离散化的研究

利用半离散的方法将两相同部件冷贮备可修系统中的μ(x)进行离散,得到两个偏微分方程,进一步利用算子半群的理论证明离散后的解是收敛于原方程的解.     

Sobolev方程的混合连续时空有限元解的误差估计

通过引入辅助变量构造Sobolev方程的混合连续时空有限元离散格式,使得该格式既利用混合法将方程降阶,又将时间和空间两个变量同时用有限元方法离散,从而获得时空形式高精度数值模型.证明了Sobolev方程混合时空有限元解的存在唯一性、稳定性,并利用时间和空间投影算子推导出时空数值解的误差估计.     

Sine-Gordon方程的多辛Leap-frog格式

非线性发展方程由于具有多种形式的解析解而吸引着众多的研究者,借助多辛保结构理论研究了Sine-Gordon方程的多辛算法．利用Hamilton变分原理,构造出了sine-Gordon方程的多辛格式;采用显辛离散方法得到了Leap-frog多辛离散格式,该格式满足多辛守恒律;数值结果表明leap-frog多辛离散格式能够精确地模拟sine-Gordon方程的孤子解和周期解,模拟结果证实了该离散格式具有良好的数值稳定性．     

四阶线性抛物型积分-微分方程的混合间断时空有限元法

构造四阶抛物型积分-微分方程的混合间断时空有限元格式,利用混合有限元方法将高阶方程降阶,利用空间连续而时间允许间断的时空有限元方法离散方程,证明离散解的稳定性,存在唯一性和收敛性.     

一类四阶抛物型积分-微分方程的混合间断时空有限元法

构造四阶抛物型积分-微分方程的混合间断时空有限元格式,利用混合有限元方法将高阶方程降阶,利用空间连续而时间允许间断的时空有限元方法离散方程,证明离散解的稳定性,存在唯一性和收敛性.     

关于L~1空间积分方程特征值探究

在L~1空间中讨论积分方程的特征值问题,利用连续函数的性质,对已有的部分离散法进一步改进,并举出具体算例将两种算法通过Matlab作图进行对比,说明改善后的部分离散法求出的数值解更佳.     

带广义边界条件的四阶抛物型方程的混合间断时空有限元法

构造具有广义边界条件的四阶线性抛物型方程的混合间断时空有限元格式,利用混合有限元方法将高阶方程降阶,利用空间连续而时间允许间断的时空有限元方法离散方程,证明了离散解的存在唯一性,稳定性和收敛性,并给出数值算例验证了方法的有效性.     

求解三维第一类Fredholm积分方程的GMRES法

利用数值求积公式,将三维第一类Fredholm积分方程进行离散,通过引入正则化方法,将离散后的积分方程转化为一离散适定问题,通过广义极小残余算法得到了其数值解．数值模拟结果表明该方法的可行有效性．     

一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题，依据传统的基于数值信息的FCM聚类算法的思路，提出了一种新的聚类分析算法。章首先描述了具有区间数多指标信息的聚类分析问题；其次给出了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理；然后给出了基于区间数多指标信息的FCM聚类算法的计算步骤。该算法的特点是聚类中心的表现形式为精确的数值，给出的两个定理说明了该聚类算法的收敛性。最后，通过给出一个算例说明了本给出的聚类算法。     

Affine-scaling for linear programs with free variables

The affine-scaling modification of Karmarkar's algorithm is extended to solve problems with free variables. This extended primal algorithm is used to prove two important results. First the geometrically elegant feasibility algorithm proposed by Chandru and Kochar is the same algorithm as the one obtained by appending a single column of residuals to the constraint matrix. Second the dual algorithm as first described by Adler et al., is the same as the extended primal algorithm applied to the dual.     

A contraction proximal point algorithm with two monotone operators

It is a known fact that the method of alternating projections introduced long ago by von Neumann fails to converge strongly for two arbitrary nonempty, closed and convex subsets of a real Hilbert space. In this paper, a new iterative process for finding common zeros of two maximal monotone operators is introduced and strong convergence results associated with it are proved. If the two operators are subdifferentials of indicator functions, this new algorithm coincides with the old method of alternating projections. Several other important algorithms, such as the contraction proximal point algorithm, occur as special cases of our algorithm. Hence our main results generalize and unify many results that occur in the literature.     

A hybrid genetic algorithm with dominance properties for single machine scheduling with dependent penalties

In this paper, a hybrid genetic algorithm is developed to solve the single machine scheduling problem with the objective to minimize the weighted sum of earliness and tardiness costs. First, dominance properties of (the conditions on) the optimal schedule are developed based on the switching of two adjacent jobs i and j. These dominance properties are only necessary conditions and not sufficient conditions for any given schedule to be optimal. Therefore, these dominance properties are further embedded in the genetic algorithm and we call it genetic algorithm with dominance properties (GADP). This GADP is a hybrid genetic algorithm. The initial populations of schedules in the genetic algorithm are generated using these dominance properties. GA can further improve the performance of these initial solutions after the evolving procedures. The performances of hybrid genetic algorithm (GADP) have been compared with simple genetic algorithm (SGA) using benchmark instances. It is shown that this hybrid genetic algorithm (GADP) performs very well when compared with DP or SGA alone.     

Total completion time with makespan constraint in no-wait flowshops with setup times

The m-machine no-wait flowshop scheduling problem with the objective of minimizing total completion time subject to the constraint that the makespan value is not greater than a certain value is addressed in this paper. Setup times are considered non-zero values, and thus, setup times are treated as separate from processing times. Several recent algorithms, an insertion algorithm, two genetic algorithms, three simulated annealing algorithms, two cloud theory-based simulated annealing algorithms, and a differential evolution algorithm are adapted and proposed for the problem. An extensive computational analysis has been conducted for the evaluation of the proposed algorithms. The computational analysis indicates that one of the nine proposed algorithms, one of the simulated annealing algorithms (ISA-2), performs much better than the others under the same computational time. Moreover, the analysis indicates that the algorithm ISA-2 performs significantly better than the earlier existing best algorithm. Specifically, the best performing algorithm, ISA-2, proposed in this paper reduces the error of the existing best algorithm in the literature by at least 90% under the same computational time. All the results have been statistically tested.     

Evolutionary Approaches to DNA Sequencing with Errors

In the paper, two evolutionary approaches to the general DNA sequencing problem, assuming both negative and positive errors in the spectrum, are compared. The older of them is based on the idea of genetic approach and is enhanced by a greedy algorithm. The newly proposed algorithm combines the tabu search and the scatter search methods. After conducting experiments with random and coding DNA sequences, our results suggest that the tabu and scatter search algorithm finds solutions of higher quality and more reliably than the genetic algorithm.     

The one-to-one shortest-path problem: An empirical analysis with the two-tree Dijkstra algorithm

Four new shortest-path algorithms, two sequential and two parallel, for the source-to-sink shortest-path problem are presented and empirically compared with five algorithms previously discussed in the literature. The new algorithm, S22, combines the highly effective data structure of the S2 algorithm of Dial et al., with the idea of simultaneously building shortest-path trees from both source and sink nodes, and was found to be the fastest sequential shortest-path algorithm. The new parallel algorithm, PS22, is based on S22 and is the best of the parallel algorithms. We also present results for three new S22-type shortest-path heuristics. These heuristics find very good (often optimal) paths much faster than the best shortest-path algorithm.     

An efficient algorithm for the computation of the metric average of two intersecting convex polygons, with application to morphing

Motivated by the method for the reconstruction of 3D objects from a set of parallel cross sections, based on the binary operation between 2D sets termed “metric average”, we developed an algorithm for the computation of the metric average between two intersecting convex polygons in 2D. For two 1D sets there is an algorithm for the computation of the metric average, with linear time in the number of intervals in the two 1D sets. The proposed algorithm has linear computation time in the number of vertices of the two polygons. As an application of this algorithm, a new technique for morphing between two convex polygons is developed. The new algorithm performs morphing in a non-intuitive way.     

带学习效应的两台平行机时间表长问题

研究机器带学习效应, 目标函数为时间表长的两台平行机排序问题, 问题是NP-难的. 首先建立了求解该问题最优解的整数规划模型. 其次, 基于模拟退火算法给出了该问题的近似算法SA, 并证明了该算法依概率1 全局收敛到最优解. 最后, 通过数值模拟对所提出的算法进行了性能分析. 数值模拟结果表明, 近似算法SA可以达到最优值的99%, 准确度高, 算法较有效.     

An algorithm for solving new trust region subproblem with conic model

The new trust region subproblem with the conic model was proposed in 2005, and was divided into three different cases. The first two cases can be converted into a quadratic model or a convex problem with quadratic constraints, while the third one is a nonconvex problem. In this paper, first we analyze the nonconvex problem, and reduce it to two convex problems. Then we discuss some dual properties of these problems and give an algorithm for solving them. At last, we present an algorithm for solving the new trust region subproblem with the conic model and report some numerical examples to illustrate the efficiency of the algorithm.