特征向量计算的神经网络方法

矩阵特征向量计算在实际问题中有着广泛应用，本文采用神经网络计算方法来研究主元分析（ＰＣＡ）和次元分析（ＭＣＡ）问题．我们首先考虑神经元的情况（ｐ＝１），给出了求矩阵最大特征元和最小特征元的算法。然后对多神经元性形（ｐ〉１），给出了抽取矩阵主元和次元的算法．和目前许多元知的算法不一样，在我们ＰＣＡ的算法中发迹矩阵的负号就能够得到ＭＣＡ问题的解。     

半无限极大极小问题的全局收敛方法

利用广义伪方向导数，在较弱的条件下，给出了半无限极大极小问题（P）的全局收敛性理论算法模型；利用离散策略给出了问题（P）全局收敛的可实现算法．数值结果表明本文给出的可实现算法是有效的．     

求解场问题的一种新算法

基于文（１）（２），本文给出了一种计算场问题的新算法－拓扑有限元Ｇｉｖｅｎｓ算法，该方法利用了拓扑有限元的特性及快速Ｇｉｖｅｎｓ变换。算例结果与实验吻合很好。     

求解线性方程组和一种迭代解法

本文对任意线性方程组ＡＸ＝Ｂ（Ａ∈Ｒ（ｎ×ｍ），Ｂ∈Ｒｎ），在文［１］基础上给出了一种迭代算法。其收敛速度比文［１］方法快，并证明了该算法的收敛性。最后，通过几个算例说明了本文算法的有效性。     

序约束下ARCH(0,2)模型参数估计与检验

本文研究了平稳ARCH（0，2）模型未知参数α的极大似然估计及有序约束时α的极大似然估计的渐近性质，给出了参数序关系（α1≥α2）的检验方法，并得出了似然比检验统计量的渐近分布。用二次规划的算法，给出求各种情况下参数α的极大似然估计的数值算法。     

城市交通拥挤问题的探讨

本文探讨了城市交通拥挤问题的解决方法．根据道路的拥挤状况引入畅通度的概念，量化了道路的拥挤程度．在道路的物理距离的基础上加入畅通因素把它转化为一种新的距离，这样使原有寻找最短路径的算法能继续适用．同时本文详细介绍了公路网络中信息的存储方法：Coordinate Storage（COO），Compressed Sparse Row（CSR），Compressed Sparse Column（CSC），Block Sparse Row，以及最短路径的搜索算法：Dijkstra算法和Bellman—ford算法，同时给出了Dijkstra算法步骤和它的最新改进算法．     

一类组合最优化问题及其算法

本文介绍三个新的组合最优化模型，并分别给出复杂性为Ｏ（Ｎ２）和Ｏ（Ｎ２α）的多项式算法和拟多项式算法．     

微分多项式系统的约化算法理论

本文中，作者推广了纯代数形式的特征列集理论（吴方法）为微分形式的相应理论，即建立了在机器证明了诸多微分问题中非常重要的微分多项式组的约化算法理论。引入了一些新的概念和观点使函数微分（导数）具有直观的代数几何表示。给出了Coherent条件下的特征列集的算法。给出的算法易于在计算机上实现并适合应用于广泛的微分问题，如微分方程对称计算，各种微分关系的自动推理等问题。     

求Ramsey数下界的循环巧妙图搜索算法研究

本文研究通过构造循环巧妙图而搜寻Ｒａｍｓｅｙ数下界的算法，给出了一个效率较高的算法，该算法已经编程实现，并由此得出了一个具有４６点（４，７）循环巧妙图，从而证明了ｒ（４，７）≥４７。     

松驰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、上界。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。     

整数向量卷积的一个最佳算法

在离散概率、数字信号处理及其他工程问题中,常会遇到两个向量的卷积(这里指的是循环卷积)的计算,即已知向量     

Combination of trust region method and simplicial decomposition for convex constrained nonlinear optimization

The algorithm presented in this article incorporates the trust region method (TR) into the restricted decomposition algorithm for convex-constrained nonlinear problems (RSDCC) to solve the master problem of RSDCC. The global convergence is proved. The computational comparison between the presented algorithm and RSDCC is given. The results show that the former is much better than the latter.     

一种基于LVI求解二次规划问题的数值算法

给出并研究了一种数值算法(简称94LVI算法),用于求解带等式和双端约束的二次规划问题. 这类带约束的二次规划问题首先被转换为线性变分不等式问题,该问题等价于分段线性投影等式.接着使用94LVI算法求解上述分段线性投影等式,从而得到QP问题的最优解. 进一步给出了94LVI算法的全局收敛性证明. 94LVI算法与经典有效集算法的对比实验结果证实了给出的94LVI算法在求解二次规划问题上的高效性与优越性.     

Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

A generalization of the particle swarm optimization (PSO) algorithm is presented in this paper. The novel optimizer, the Generalized PSO (GPSO), is inspired by linear control theory. It enables direct control over the key aspects of particle dynamics during the optimization process. A detailed theoretical and empirical analysis is presented, and parameter-tuning schemes are proposed. GPSO is compared to the classical PSO and genetic algorithm (GA) on a set of benchmark problems. The results clearly demonstrate the effectiveness of the proposed algorithm. Finally, an application of the GPSO algorithm to the fine-tuning of the support vector machines classifier for electrical machines fault detection is presented.     

A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop

In this paper a new mixed-integer linear programming (MILP) model is proposed for the multi-processor open shop scheduling (MPOS) problems to minimize the makespan with considering independent setup time and sequence dependent removal time. A hybrid imperialist competitive algorithm (ICA) with genetic algorithm (GA) is presented to solve this problem. The parameters of the proposed algorithm are tuned by response surface methodology (RSM). The performance of the algorithm to solve small, medium and large sized instances of the problem is evaluated by introducing two performance metrics. The quality of obtained solutions is compared with that of the optimal solutions for small sized instances and with the lower bounds for medium sized instances. Also some computational results are presented for large sized instances.     

Absolute approximation of Tukey depth: Theory and experiments

A Monte Carlo approximation algorithm for the Tukey depth problem in high dimensions is introduced. The algorithm is a generalization of an algorithm presented by Rousseeuw and Struyf (1998) [20]. The performance of this algorithm is studied both analytically and experimentally.     

Analysis of mathematical programming problems prior to applying the simplex algorithm

Large practical linear and integer programming problems are not always presented in a form which is the most compact representation of the problem. Such problems are likely to posses generalized upper bound(GUB) and related structures which may be exploited by algorithms designed to solve them efficiently. The steps of an algorithm which by repeated application reduces the rows, columns, and bounds in a problem matrix and leads to the freeing of some variables are first presented. The ‘unbounded solution’ and ‘no feasible solution’ conditions may also be detected by this. Computational results of applying this algorithm are presented and discussed. An algorithm to detect structure is then described. This algorithm identifies sets of variables and the corresponding constraint relationships so that the total number of GUB-type constraints is maximized. Comparisons of computational results of applying different heuristics in this algorithm are presented and discussed.     

The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming

A new deterministic algorithm for solving convex mixed-integer nonlinear programming (MINLP) problems is presented in this paper: The extended supporting hyperplane (ESH) algorithm uses supporting hyperplanes to generate a tight overestimated polyhedral set of the feasible set defined by linear and nonlinear constraints. A sequence of linear or quadratic integer-relaxed subproblems are first solved to rapidly generate a tight linear relaxation of the original MINLP problem. After an initial overestimated set has been obtained the algorithm solves a sequence of mixed-integer linear programming or mixed-integer quadratic programming subproblems and refines the overestimated set by generating more supporting hyperplanes in each iteration. Compared to the extended cutting plane algorithm ESH generates a tighter overestimated set and unlike outer approximation the generation point for the supporting hyperplanes is found by a simple line search procedure. In this paper it is proven that the ESH algorithm converges to a global optimum for convex MINLP problems. The ESH algorithm is implemented as the supporting hyperplane optimization toolkit (SHOT) solver, and an extensive numerical comparison of its performance against other state-of-the-art MINLP solvers is presented.     

对称r-循环矩阵的快速算法和并行算法

借助于快速付立叶变换(FFT),给出了一种判断对称r-循环线性系统是否有解的快速算法,并且在有解的情况下求出其解,该算法的计算复杂度为O(nlogn),且具有很好的并行性,若使用n台处理机并行处理该算法则只需要O(logn)步.当r=0时,对称r-循环矩阵变成一个上三角型Hankel矩阵,我们也给出了此类矩阵求逆的一种算法.最后将该算法推广到线性同余系统,其运算量仅为O(nlogn).     

一个修正的PVT算法

对Fkshima(1998）所提出的PVT算法给出一种修正算法，称为修正PVT算法，这一修正算法对PVT原算法中的并行步中的停止准则和同步步骤作了修正。修正PVT算法的停止条件对PVT原算法的停止条件弱，因此更适用于并行计算，并且计算时间比PVT原算法少。