紧框架的一个注记

假设Φ(x)∈L^2(R)且具有紧支集。令V=span^—{Φ(x—k)|k∈Z}。在这篇短文中，我们证明：如果{Φ(x—k)|k∈Z}是V的界为1的紧框架，那么{Φ(x—k)|k∈Z}一定是V的一个标准正交基。     

2000年全国高中数学联合竞赛试题及参考答案

选择题(本题满分36分,每小题6分)1.设全集是实数集,若A={x|x-2≤0},B={x|10x2-2=10x},则A∩B是(　　).　(A){2}　(B){-1}　(C){x|x≤2}　(D)解　由x-2≤0得x=2,故A={2};由10x2-2=10x得x2-x-2=0,故B={-1,2}.所以A∩B=.故选(D).2.设sinα&;gt;0,cosα&;lt;0,且sinα3&;gt;cosα3,则α3的取值范围是(　　).　(A)(2kπ+π6,2kπ+π3),k∈Z　(B)(2kπ3+π6,2kπ3+π3),k∈Z　(C)(2kπ+5π6,2kπ+π),k∈Z　(D)(2kπ+π4,2kπ+π3)∪(2kπ+5π6,2kπ+π),k∈Z解　由2kπ+π2&;lt;α&;lt;2kπ+π得2kπ3+π6&;lt;α3&;lt;2kπ3+π3,k∈Z.又　sinα3&;gt;cosα3,所以又有2kπ+π4&;lt;α3&;lt;2kπ+5π4,k∈Z.此两式的公共部分为(2kπ+π4,2kπ+π3)∪(2kπ+5π6,2kπ+π),k∈Z.故选(D).3.已知点A为双曲线x2-y2=1的左顶点,点B和点C在双曲线的右分支上,△ABC是等边三角形,则△ABC的面积是(　　).(A...     

三角函数周期的探讨

有高中“三角函数”这一章中,我们知道y =Asin(ωx + φ) (x∈R ,Aω≠0 ,A ,ω,φ为常数)与y =Acos(ωx + φ) (x∈R ,Aω≠0 ,A ,ω,φ为常数)及y =Asin2 (ωx + φ) (x∈R ,Aω≠0 ,A ,ω,φ为常数)与y =Acos2 (ωx +φ) (x∈R ,A·ω≠0 ,A ,ω,φ为常数)这些三角函数的周期.那么,三角函数y =Asinn(ωx+ φ)与y =Acosn(ωx + φ) (A·ω≠0 ,A ,ω,φ为常数x∈R)的周期又是怎样的呢?定理1　1 )函数y =sinnx (x∈R) .当n为偶数时的周期为kπ,(k∈Z ,k≠0 ) ,最小正周期为π;当n为奇数时,周期为2kπ(k∈Z ,k≠0 ) ,最小正周期为…     

正数两边竟不能平方

若sinx cosx-1>0,求x的范围。解∵sinx cosx>1>0,两边平方得 sin~2x cos~2x 2sinxcosx>1, 即sin2x>0, 2kπ<2x<2kπ π(k∈Z) 故kπ相似文献   

紧支撑正交插值的多小波和多尺度函数

本文给出一类伸缩因子为α的紧支撑正交插值多尺度函数和多小波的构造方法.设{Vj}是尺度函数Φ(x)=[φ1(x),φ2(x),…,φa(x)]T生成的多分辨分析,Vj(?)L2(R)是{a-j/2φ(?)(ajx-k),k∈Z,(?)=1,2,…,a)线性扩张构成的子空间,其插值性是指φ1(x),φ2(x),…,φa(x)满足φj(k+(?)/a)=δk,0δj,e,j,(?)∈{1,2,…,a).当Φ(x)是正交插值的,则多分辨分析的分解或重构系数能用采样点表示而不需要用计算内积的方法产生.基于此,我们建立多小波采样定理,即如果一个连续信号f(x)∈VN,则f(x)=∑i=0a-1∑k∈Zf(k/aN+i/aN+1)φi+1(aNx-k),并给出对应多小波的显式构造公式.更进一步,证明了本文构造的多小波也有插值性.最后,还给出一个构造算例.     

多尺度分析生成元的刻画

本文将给出多尺度分析生成元的一种完全刻画.将证明:函数φ∈L~2(R)是二进多尺度分析生成元的充要条件是(1)存在{a_k}∈l~2,φ(x)=∑_(k∈Z)a_kφ(2x-k);(2)存在正数A相似文献   

E-紧框架小波来自MRA的特征刻画

设E=■或■,■(x)∈L~2(R~2)且■_(jk)(x)=2■(E~jx-k),其中j∈Z,k∈Z~2.若{■_(jk)|jJ∈Z,k∈Z~2}构成L~2(R~2)的紧框架,则称■(x)为E-紧框架小波.本文给出E-紧框架小波是MRA E-紧框架小波的一个充要条件,即E紧框架小波■来自多尺度分析当且仅当线性空间F_■(ξ)的维数为0或1,其中F_■(ξ)=■(ξ)|j■1},■_j(ξ)={■((E~T)~j(ξ+2kπ))}_(k∈EZ~2,j■1。     

一个多元绝对值函数的最小值

对于函数F(x1,x2,…,xn)=|a1x1 a2x2 … anxn A|,由绝对值的意义知F(x1,x2,…,xn)≥0.特别,当ai,xi,A∈Z(i=1,2,…,n)时,该函数有更精确的下界,本文将给出这个结论.定理设F(x1,x2,…,xn)=|ni=1aixi A|,ai,xi,ki,A,m∈Z,(a1,a2,…,an)=d,ai=kid,(k1,k2,…,kn)=1,A=md r,0≤r相似文献   

2006年高考数学全国卷Ⅰ

一、选择题:共12小题,每小题5分,共60分.1.设集合M={x|x2-x<0},N={x||x|<2},则A.M∩N=B.M∩N=MC.M∪N=MD.M∪N=R2.已知函数y=ex的图像与函数y=f(x)的图像关于直线y=x对称,则A.f(2x)=e2x(x∈R)B.f(2x)=ln2·lnx(x>0)C.f(2x)=2ex(x∈R)D.f(2x)=lnx+ln2(x>0)3.双曲线mx2+y2=1的虚轴长是实轴长的2倍,则m=A.-41B.-4C.4D.414.如果复数(m2+i)(1+mi)是实数,则实数=A.1B.-1C.2D.-25.函数f(x)=tanx+4π的单调增区间为A.kπ-2π,kπ+2π,k∈ZB.(kπ,(k+1)π),k∈ZC.kπ-34π,kπ+4π,k∈ZD.kπ-4π,kπ+34π,k∈Z6.△ABC的内角A、B、…     

高阶导算子为厄米特算子的一种充要条件

对于A∈C_(n×n),~n?A的k阶导算子δ_n~((k))(A)的正交数值域是指W~⊥(δ_n~((k))(A))={E_k(x)|x∈W_n(A)} ,1≤k≤n,其中E_k(x)为C~n上第k个初等对称函数,W_n(A)={digU~*AU|U∈u_n}。本文证明了当3≤k≤n时,δ_n~((k))(A)为厄米特算子的充要条件是W~⊥(δ_n~((k))(A))?R。     

小波分析中一个逆问题

本文证明:如果来自多尺度分析(伸缩因子为矩阵)的小波是标准正交的,那么相对应的尺度函数也是标准正交的,其中函数f_s(x)∈L~2(R~n)(s=1,2,…,r,r是正整数)的标准正交性是指f_s(x)的整平移所构成的函数族为L~2(R~n)的标准正交系。结果表明,如果我们想从多尺度分析出发构造正交小波,那么该多尺度分析必须有正交尺度函数。     

二维周期基数插值小波尺度函数

设f（x）是一个Fourier系数为正的周期函数，我们构造了关f（x）的二维周期基数插值小波的尺度函数，并得到了一些对构造小波函数有重要意义的性质．     

Series Representation of Daubechies' Wavelets

1,Iotroduction.InthispaPerwe8tudytherepresentationofDaubechies'wavelets.DaubechiesI1]constructedaf4milyofcompartlysupportedregularscallngfUnctionsrk.(x)andtheassoci4tedregularwpeletsop.(x)(N32):where4.eL'(R)definedbythep0lyn0mia:withZq.(k)=1'q.(k)ER,k=0,1,')N-1.Itisknownthat[1]f0reachN32,k=Osuppgh.=[0,2N-l],suppop.=[-(N-1),N]andthewaveletop.generatesbyitsdilatiOnsandtranslati0nsan0rth0rn0rmalbasis{m.(2ix-k)}i,k6Z0fL'(R).Thefunctionsrk.andop.havebeenprovedtobeveryusefulinnumericalanal…     

小波伽辽金方法应用于变系数波动方程

我们考虑问题K（x）uxx=ua.0＜X〈1，t≥0，其中K（x）≥a≥0，u（0，t）=g，ix（0，t）=0．这是一个不适当的方程，因为当解存在时在边界g上一个小的扰动将对它的解造成很大的改变．我们考虑存在解u（x，·）∈L^2（R）用小波伽辽金方法和Meyer多分辨分析去滤掉高频部分，从而在尺度空间Vj上得到适定的近似解．我们也可以得到问题的准确解与它在Vj上的正交投影之间的误差估计．     

Scaling dynamics for solvable coagulation equations with dust and gel

We study limiting behavior of rescaled size distributions that evolve by Smoluchowski's rate equations for coagulation, with rate kernel K=2, x+y or xy. We find that the dynamics naturally extend to probability distributions on the half-line with zero and infinity appended, representing populations of clusters of zero and infinite size. The “scaling attractor” (set of subsequential limits) is compact and has a Levy-Khintchine-type representation that linearizes the dynamics and allows one to establish several signatures of chaos. In particular, for any given solution trajectory, there is a dense family of initial distributions (with the same initial tail) that yield scaling trajectories that shadow the given one for all time. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Almost sure,L_1-and L_2-growth behavior of supercritical multi-type continuous state and continuous time branching processes with immigration

Under a first order moment condition on the immigration mechanism, we show that an appropriately scaled supercritical and irreducible multi-type continuous state and continuous time branching process with immigration(CBI process) converges almost surely. If an x log(x) moment condition on the branching mechanism does not hold, then the limit is zero. If this x log(x) moment condition holds, then we prove L_1 convergence as well. The projection of the limit on any left non-Perron eigenvector of the branching mean matrix is vanishing.If, in addition, a suitable extra power moment condition on the branching mechanism holds, then we provide the correct scaling for the projection of a CBI process on certain left non-Perron eigenvectors of the branching mean matrix in order to have almost sure and L_1 limit. Moreover, under a second order moment condition on the branching and immigration mechanisms, we prove L_2 convergence of an appropriately scaled process and the above-mentioned projections as well. A representation of the limits is also provided under the same moment conditions.     

Generalized low pass filters and MRA frame wavelets

A tight frame wavelet ψ is an L ²(ℝ) function such that {ψ jk(x)} = {2^j/2 ψ(2 ^j x −k), j, k ∈ ℤ},is a tight frame for L ² (ℝ).We introduce a class of “generalized low pass filters” that allows us to define (and construct) the subclass of MRA tight frame wavelets. This leads us to an associated class of “generalized scaling functions” that are not necessarily obtained from a multiresolution analysis. We study several properties of these classes of “generalized” wavelets, scaling functions and filters (such as their multipliers and their connectivity). We also compare our approach with those recently obtained by other authors.     

求解一般界约束优化问题的积极集信赖域方法

本文提出了一种求解一般界约束优化问题的新方法. 每步迭代分为两个阶段. 在第一阶段, 从 当前迭代点x_k 出发, 沿着经过仿射变换后的梯度步, 得到试探点x_k¹, 记录下它的积极集. 这里用到的仿射变换矩阵不仅依赖于变量到边界的距离, 还依赖于当前迭代点的梯度以及该步迭代中的信赖域半 径. 在第二阶段, 从x_k¹ 出发, 通过在积极约束的零空间里面求解一个信赖域子问题得到新的试探点. 然后判断是否接受这个试探点作为下一个迭代点. 文中证明了算法的全局收敛性, 并且迭代点列的每 个聚点都是一阶稳定点. 文中还对国际著名的CUTEr 算例库中所有的界约束优化问题进行了测试. 数值结果表明我们的方法是有效的, 并且可以与L-BFGS-B 方法相媲美.     

Tight frames of multidimensional wavelets

In this paper we deal with multidimensional wavelets arising from a multiresolution analysis with an arbitrary dilation matrix A, namely we have scaling equations $$\varphi ^s (x) = \sum\limits_{k \in \mathbb{Z}^n } {h_k^s \sqrt {|\det A|} \varphi ^1 } (Ax - k) for s = 1, \ldots ,q,$$ where ?¹ is a scaling function for this multiresolution and ?², …, ?^q (q=|det A |) are wavelets. Orthogonality conditions for ?¹, …, ?^q naturally impose constraints on the scaling coefficients $\{ h_k^s \} _{k \in \mathbb{Z}^n }^{s = 1, \ldots ,q} $ , which are then called the wavelet matrix. We show how to reconstruct functions satisfying the scaling equations above and show that ?², …, ?^q always constitute a tight frame with constant 1. Furthermore, we generalize the sufficient and necessary conditions of orthogonality given by Lawton and Cohen to the case of several dimensions and arbitrary dilation matrix A.     

A WAVELET METHOD FOR THE FREDHOLM INTEGRO-DIFFERENTIAL EQUATIONS WITH CONVOLUTION KERNEL

1.IntroductionInthispaper,westudytheFredholmintegro-differentialequationbythewaveletmethod.Theapplicationsoftheequationinimagerestorationcouldbefoundin[101.ForthehistoryofnumericalmethodsfortheFredholmintegro-differentialequations,wereferto[4].FOllow...