树的最大特征值的上界的一个注记

设T是一个树,V是T的顶点集．记dv是υ∈V的度,△是T的最大顶点度．设υ∈V且dw=1．记k=ew+1,这里ew是w的excentricity．设δj′= max{dυ:dist(υ,w)=j},j=1,2,…,k-2,我们证明和这里μ1(T)和λ1(T)分别是T的Laplacian矩阵和邻接矩阵的最大特征值．特别地,记δo′=2．     

一类具有转向点的三阶微分方程的边值问题的解的高阶渐近展开

利用上下解方法,研究如下一类具有转向点的三阶微分方程的边值问题{ε~2y″′=f(t,y,ε)y″ g(t,y,ε)y′ h(t,y,ε),a相似文献   

乘方的指数与其幂的位数的关系

为了探究乘方的指数与其幂的位数的关系,定义了几个有关的新概念,并且证明了两个关于乘方以及进制进位的定理,由此建立起关于乘方以及进制进位的理论体系,其中包括进位理论中判定乘方的指数与其幂的位数是否存在周期规律的判别法,以及进位规律的求解法和四条相关的性质.     

计算Hamilton矩阵特征值的一个稳定的有效的保结构的算法

提出了一个稳定的有效的保结构的计算Hamilton矩阵特征值和特征不变子空间的算法,该算法是由SR算法改进变形而得到的。在该算法中,提出了两个策略,一个叫做消失稳策略,另一个称为预处理技术。在消失稳策略中,通过求解减比方程和回溯彻底克服了Bunser Gerstner和Mehrmann提出的SR算法的严重失稳和中断现象的发生,两种策略的实施的代价都非常低。数值算例展示了该算法比其它求解Hamilton矩阵特征问题的算法更有效和可靠。     

数的蒙蔽与形的误导

数与形是一个数学问题的两个层面,数能精细,形可直观,但同学们也须注意,如果你在解题时,出现了疏漏,数也可能出现貌似精细的蒙蔽,形也可以导致似乎明了的误导.本文通过两个典型例子,提醒同学们在学习     

函数的对称性和周期性的关系

“函数”一章的内容贯穿于高中数学的始终,历来是数学高考考查的一个难点和热点,要求学生熟练掌握函数的性质．但在学习这一章过程中,许多同学被函数的若干性质弄的头昏脑涨．事实上,只要把握其中的关系,也就不困难了．     

与一类算术函数关联的矩阵的行列式的下界

设f为一个算术函数,S={x 1,…,x n}为一个n元正整数集合.称S为gcd-封闭的, 如果对于任意1 i,j n,均有(x i,x j)∈S.以 ={y 1,…,y m}表示包含S的最小gcd-封闭的正整数集合. 设(f(x i,x j))表示一个n×n矩阵, 其(i,j)项为f在x i与x j的最大公因子(x i,x j)处的值. 设(f[x i,x j])表示一个n×n矩阵, 其(i,j)项为f在x i与x j的最小公倍数[x i.xj]处的值. 本文证明了: (i) 如果f∈C s ={f:(f*μ)(d)>0, x∈S,d|x},这里f*μ表示f与μ的Dirichlet乘积,μ表示M bius函数,那么 并且(1)取等号当且仅当S=;(ii)如果f为乘法函数,并且 ∈Cs,那么 并且(2)取等号当且仅当S= .不等式(1)和(2)分别改进了Bourque与Ligh在1993年和1995年所得到的结果.     

自由群的直积的检验元素的注记

本文研究了自由群的直积的检验元素,通过对直积的自同态的分解,得到了直积中的元素为检验元素的充分必要条件,改进了O'neill和Turner的结果.此外,构造了两类具体的检验元素.     

基于Spearman的rho的Copula参数模型的选择

从Spearman的rho与Kendall的tau的关系入手,讨论了一类二元Copula参数模型的选择问题.由于这类二元Copula参数模型的Spearman的rho与Kendall的tau存在某种函数关系,模型选择问题转化为了曲线拟合检验问题.对于正态Copula、Frank-Copula,FGM-Copula、B11-Copula等这类Copula参数模型,说明了两种情况下进行模型选择的方法,并对中国股市的上证指数与深证综指作了实证分析,结果表明两者存在着较强的正相关性,相关性模型选取B11-Copula参数模型最合适.     

由自然数的因子构成的半群

本文首先介绍了半群、群的定义,研究了自然数n取何值时,通过定义相应的代数运算,使得它的因子集构成半群、群,从而为半群的代数理论提供了一个有趣的实例.     

A POLYNOMIAL PREDICTOR-CORRECTOR INTERIOR-POINT ALGORITHM FOR CONVEX QUADRATIC PROGRAMMING

This article presents a polynomial predictor-corrector interior-point algorithm for convex quadratic programming based on a modified predictor-corrector interior-point algorithm. In this algorithm, there is only one corrector step after each predictor step, where Step 2 is a predictor step and Step 4 is a corrector step in the algorithm. In the algorithm, the predictor step decreases the dual gap as much as possible in a wider neighborhood of the central path and the corrector step draws iteration points back to a narrower neighborhood and make a reduction for the dual gap. It is shown that the algorithm has O(n~(1/2)L) iteration complexity which is the best result for convex quadratic programming so far.     

一类凸规划的多项式预估校正内点法

1、引言 1990年由Mehrotra对线性规划问题提出了一个称为预估校正的方法,并在1992年给出了其数值算法．1993年Mizuno,Todd和Y．Ye．给出了改进的预估校正内点法,使得一个预估步后只跟一个校正步．1994年F．A．Potra给出了不可行预估校正内点法,使得可以从一个不可行的初始点开始算法的迭代,并证明了其为二次收敛．     

认股权证隐含波动率迭代算法的改进

为了有效估计认股权证的隐含波动率,以及有利于观测权证的市场风险,对常用的迭代算法进行改进可加快计算速度.利用认股权证的Delta、Vega和Theta值以及多元泰勒公式导出认股权证隐含波动率变化与权证价格变化关系的不等式.根据导出的这个不等式给出了具体算法.实证结果说明这一算法改进了原有的算法,从而这个算法解决了原来的随意给定一个价格后不断修正的算法.     

A hybrid algorithm for nonlinear minimax problems

In this paper, a hybrid algorithm for solving finite minimax problem is presented. In the algorithm, we combine the trust-region methods with the line-search methods and curve-search methods. By means of this hybrid technique, the algorithm, according to the specific situation at each iteration, can adaptively performs the trust-region step, line-search step or curve-search step, so as to avoid possibly solving the trust-region subproblems many times, and make better use of the advantages of different methods. Moreover, we use second-order correction step to circumvent the difficulties of the Maratos effect occurred in the nonsmooth optimization. Under mild conditions, we prove that the new algorithm is of global convergence and locally superlinear convergence. The preliminary experiments show that the new algorithm performs efficiently.     

一个带固定步长的ODE型算法

提出了一种新的求解无约束优化问题的ODE型方法,其特点是:它在每次迭代时仅求解一个线性方程组系统来获得试探步;若该试探步不被接受,算法就沿着该试探步的方向求得下一个迭代点,其中步长通过固定公式计算得到.这样既避免了传统的ODE型算法中为获得可接受的试探步而重复求解线性方程组系统,又不必执行线搜索,从而减少了计算量.在适当的条件下,还证明了新算法的整体收敛性和局部超线性收敛性.数值试验结果表明:提出的算法是有效的.     

Adaptive stochastic approximation algorithm

In this paper, stochastic approximation (SA) algorithm with a new adaptive step size scheme is proposed. New adaptive step size scheme uses a fixed number of previous noisy function values to adjust steps at every iteration. The algorithm is formulated for a general descent direction and almost sure convergence is established. The case when negative gradient is chosen as a search direction is also considered. The algorithm is tested on a set of standard test problems. Numerical results show good performance and verify efficiency of the algorithm compared to some of existing algorithms with adaptive step sizes.     

A hybrid approach for the integration of a rational function

A hybrid integration algorithm obtaining an indefinite integral of a rational function (say q/r, q and r are polynomials) with floating-point but real coefficients is proposed. The algorithm consists of four steps and is based on combinations of symbolic and numeric computations (hybrid computation). The first step is a hybrid preprocessing stage. An integrand is decomposed into rational and logarithmic parts by using an approximate Horowitz' method which allows floating-point coefficients. Here, we replace the Euclidean GCD algorithm with an approximate-GCD algorithm which was proposed by Sasaki and Noda recently. It is easy to integrate the rational part. The logarithmic part is integrated numerically in the second step. Zeros of a denominator of it are computed by the numerical Durand-Kerner method which computes all zeros of a polynomial equation simultaneously. The integrand is then decomposed into partial fractions in the third step. Coefficients of partial fractions are determined by residue theory. Finally, in the fourth step, partial fractions are transformed into the resulting indefinite integral by using well-known rules of integrals. The hybrid algorithm proposed here gives both indefinite integrals and accurate values of definite integrals. Numerical errors in the hybrid algorithm depend only on errors in the second step. The algorithm evaluates some problems where numerical methods are inefficient or incapable, or a pure symbolic method is theoretically insufficient.     

Parallel Interior Point Schemes for Solving Multistage Convex Programming

The predictor–corrector interior-point path-following algorithm is promising in solving multistage convex programming problems. Among many other general good features of this algorithm, especially attractive is that the algorithm allows possibility to parallelise the major computations. The dynamic structure of the multistage problems specifies a block-tridiagonal system at each Newton step of the algorithm. A wrap-around permutation is then used to implement the parallel computation for this step.     

水平线性互补问题的广义中心路径跟踪算法

对水平线性互补问题提出了一种广义中心路径跟踪算法.任意的原始-对偶可行内点均可作为算法的初始点.每步迭代选择“仿射步”与“中心步”的凸组合为新的迭代方向,采用使对偶间隙尽可能减小的最大步长.算法的迭代复杂性为O(√nL).     

On the Global Convergence of a Projective Trust Region Algorithm for Nonlinear Equality Constrained Optimization

A trust-region sequential quadratic programming (SQP) method is developed and analyzed for the solution of smooth equality constrained optimization problems. The trust-region SQP algorithm is based on filter line search technique and a composite-step approach, which decomposes the overall step as sum of a vertical step and a horizontal step. The algorithm includes critical modifications of horizontal step computation. One orthogonal projective matrix of the Jacobian of constraint functions is employed in trust-region subproblems. The orthogonal projection gives the null space of the transposition of the Jacobian of the constraint function. Theoretical analysis shows that the new algorithm retains the global convergence to the first-order critical points under rather general conditions. The preliminary numerical results are reported.