A vector Chebyshev algorithm

We consider polynomials orthogonal relative to a sequence of vectors and derive their recurrence relations within the framework of Clifford algebras. We state sufficient conditions for the existence of a system of such polynomials. The coefficients in the above relations may be computed using a cross-rule which is linked to a vector version of the quotient-difference algorithm, both of which are proved here using designants. An alternative route is to employ a vector variant of the Chebyshev algorithm. This algorithm is established and an implementation presented which does not require general Clifford elements. Finally, we comment on the connection with vector Padé approximants. This revised version was published online in June 2006 with corrections to the Cover Date.     

Speeding up the FMMR perfect sampling algorithm: A case study revisited

In a previous paper by the second author, two Markov chain Monte Carlo perfect sampling algorithms—one called coupling from the past (CFTP) and the other (FMMR) based on rejection sampling—are compared using as a case study the move‐to‐front (MTF) self‐organizing list chain. Here we revisit that case study and, in particular, exploit the dependence of FMMR on the user‐chosen initial state. We give a stochastic monotonicity result for the running time of FMMR applied to MTF and thus identify the initial state that gives the stochastically smallest running time; by contrast, the initial state used in the previous study gives the stochastically largest running time. By changing from worst choice to best choice of initial state we achieve remarkable speedup of FMMR for MTF; for example, we reduce the running time (as measured in Markov chain steps) from exponential in the length n of the list nearly down to n when the items in the list are requested according to a geometric distribution. For this same example, the running time for CFTP grows exponentially in n. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2003     

APPROXIMATION ALGORITHM FOR MAX-BISECTION PROBLEM WITH THE POSITIVE SEMIDEFINITE RELAXATION

Using outward rotations, we obtain an approximation algorithm for Max-Bisection problem, i.e., partitioning the vertices of an undirected graph into two blocks of equal cardinality so as to maximize the weights of crossing edges. In many interesting cases, the algorithm performs better than the algorithms of Ye and of Halperin and Zwick. The main tool used to obtain this result is semidefinite programming.     

AN ALGORITHM FOR JORDAN CANONICAL FORM OF A QUATERNION MATRIX

In this paper, we first introduce a concept of companion vector, and study the Jordan canonical forms of quaternion matrices by using the methods of complex rep resentation and companion vector, not only give out a practical algorithm for Jordan canonical form J of a quaternion matrix A, but also provide a practical algorithm for corresponding nonsingular matrix P with P-1AP = J.     

离散点集上的向量有理插值算法与特征性质

1引言 给出R~2中离散点集     

基于L~1度量分析的图象分解与重构算法

Mallat‘s decompositon and reconstruction algorithms are very important in the the field of wavelet theory and its applications to signal processing.Wavelet Anal-ysis,which is based on L^2(R) space,can eliminate redundancy of signals with the help of orthogonality and characterize the processing precision with the meansquare error.In the recent years,it is understood that the mean square measuredoes not match human visual sensitivity well.From the point of view,R.DeVore studied L^1 measure instead.Similarly,considering the principles of image com-pression,Yang introduced and dealt with orthogonality in L^1 space based on thebest approximation theory,and consequently established the corresponding decom-position and reconstruction algorithms for signals.In this paper,error analyses for the algorithms above are taken and the selection of the best parameters in the algorithms are discussed in detail.Finally,the algorithms are compared with the classical Haar and Daubechies‘‘s orthogonal wavelets based on the singal-to-noiseratio data computed.     

空间有理参数多项式曲线的整数级绘制算法

讨论了空间有理曲线中心投影后导数上界的估计，基于曲线各阶差分的递推计算，给出了空间有理参数多项式曲线的快速绘制算法．算法只用到整数的加减法，效率高．     

水资源可再生能力综合评价的遗传加权物元模型

为了科学地计算水资源可再生能力评价指标的权重 ,使评价方法具有可操作性 ,采用主、客观相结合的赋权基点法 ,以全局收敛的格雷码加速遗传算法为工具来确定权重 ,并结合物元分析理论 ,建立了一种新的评价模型——遗传加权物元模型 .文中给出了该模型实施的详细步骤 ,对黄河流域 9个行政分区的水资源可再生能力进行了综合评价 ,取得了较好的效果 .     

基于加强型BP算法的神经网络故障诊断研究

文章给出了利用神经网络进行故障诊断的可行性、基本原理、诊断过程分析、和参数优化过程 ,并得出较为理想的诊断结果 .     

薄膜光学常数的模拟退火算法研究

在薄膜的光学参数拟合时，操作者给定的模型初值对拟合结果有很大的关系。采用广泛用于求解复杂系统优化问题的模拟退火算法来求解薄膜光学常数，降低了操作者给定拟合模型初值的要求。通过对常规的模拟退火算法以及非常快速的模拟退火方法(Very Fast Stimulated Annealing，VFSA)分析，针对性地在模型扰动及退火计划上存在的缺陷作了改进，提高了VFSA算法的计算稳健性。通过大量的拟合实践，验证了提出的改进模拟退火方法的有效性。