成组Broyden修正矩阵的紧凑形式与成组记忆修正算法

1 引言 成组型线性方程组 其中,p是适中的数值,由于其有相当的实际应用背景,人们一直在研究有效的数值方法,特别是近年来,实际问题中归结出来的成组型方程组,其规模越来越大,又具有稀疏结构,因而使用迭代法是一种有效的途径,目前使用比较多的是Krylov子空间方法中的Lanczos方法,CG方法,GMRES方法等等。这种成组型算法的建立,其基本出发点是使算法具有较少的计算量和存储量,具体体现在: 1)成组型算法在应用于问题(1.1)的求解时,也具有有限终止性性质,而其终止步数一般要比单个型算法的步数减少了户倍,由于成组型算法每迭代一步的计算量基本上等同于单个型算法使用户次的计算量,如此,算法的计算量会有明显的改善。 2)当A存储在二级(secondary)内存时,在迭代计算时需要不断地进行存取交换,由于成组型算法的迭代步数减少了户倍,如此,用在这种交换的时间也要减少户倍,相当有效。 3)由于在成组型算法中,出现的多是AX的形式,其中,故成组型算法便于计算并行化。 4)即使用于求解单个方程组,当A的少数几个极端特征值分离甚远时,这种成组型算法也有可能改善其收敛速度,如成组型的CG方法。 目前,这种成组型算法已体现出很大的实用计算价值,然而其进一步的理论分析还有待深入研究。     

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     

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.     

解线性最小二乘问题的一个新并行算法

讨论了求解无约束线性最小二乘问题的一种并行单纯形法以及对它的改进算法并行共轭梯度—单纯形法 .算法本身具有很强的并行机制 ,能够充分地发挥并行机快速省时的特点 .本文也对算法做了理论分析 ,对算法的收敛性给予了证明 (在二维情形下 ) .最后做了数值实验 (由于软硬件条件的限制 ,并行算法未能在并行计算机上实现 ,鉴于这种情况 ,我们所做的数值实验均是在串行机上完成的 )     

Space-Time Point-Process Models for Earthquake Occurrences

Several space-time statistical models are constructed based on both classical empirical studies of clustering and some more speculative hypotheses. Then we discuss the discrimination between models incorporating contrasting assumptions concerning the form of the space-time clusters. We also examine further practical extensions of the model to situations where the background seismicity is spatially non-homogeneous, and the clusters are non-isotropic. The goodness-of-fit of the models, as measured by AIC values, is discussed for two high quality data sets, in different tectonic regions. AIC also allows the details of the clustering structure in space to be clarified. A simulation algorithm for the models is provided, and used to confirm the numerical accuracy of the likelihood calculations. The simulated data sets show the similar spatial distributions to the real ones, but differ from them in some features of space-time clustering. These differences may provide useful indicators of directions for further study.     

Local convergence of collinear scaling algorithms related to direct least‐change secant update methods for minimization

Collinear scaling algorithms related to direct fixed-scale and rescaled least-change secant update methods for unconstrained minimization are derived. Theorems on local and q-superlinear convergence of these algorithms are presented. These results are extensions of those of Dennis and Walker [14] for direct least-change secant update methods. This revised version was published online in August 2006 with corrections to the Cover Date.     

Decomposition Methods for Differentiable Optimization Problems over Cartesian Product Sets

This paper presents a unified analysis of decomposition algorithms for continuously differentiable optimization problems defined on Cartesian products of convex feasible sets. The decomposition algorithms are analyzed using the framework of cost approx imation algorithms. A convergence analysis is made for three decomposition algorithms: a sequential algorithm which extends the classical Gauss-Seidel scheme, a synchronized parallel algorithm which extends the Jacobi method, and a partially asynchronous parallel algorithm. The analysis validates inexact computations in both the subproblem and line search phases, and includes convergence rate results. The range of feasible step lengths within each algorithm is shown to have a direct correspondence to the increasing degree of parallelism and asynchronism, and the resulting usage of more outdated information in the algorithms.     

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.     

Cutting Plane/Tabu Search Algorithms for Low Rank Concave Quadratic Programming Problems

In this paper, we will propose an efficient heuristic algorithm for solving concave quadratic programming problems whose rank of the objective function is relatively small. This algorithm is a combination of Tuy's cutting plane to eliminate the feasible region and a kind of tabu-search method to find a good vertex. We first generate a set of V of vertices and select one of these vertices as a starting point at each step, and apply tabu-search and Tuy's cutting plane algorithm where the list of tabu consists of those vertices eliminated by cutting planes and those newly generated vertices by cutting planes. When all vertices of the set V are eliminated, the algorithm is terminated. This algorithm need not converge to a global minimum, but it can work very well when the rank is relatively small (up to seven). The incumbent solutions are in fact globally optimal for all tested problems. We also propose an alternative algorithm by incorporating Rosen's hyperrectangle cut. This algorithm is more efficient than the combination of Tuy's cutting plane and tabu-search.