细分树的L_p范数或拟范数平均大小和小波包的渐近性态
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摘要
本文研究了一般整数扩张矩阵向量细分方程所生成的细分树,亦即,当整数扩张矩阵M是一个满足lim_(n→∞)M~(-n)=0时的s×s整数扩张矩阵的可细分函数产生的细分树的L_p范数或拟范数范数平均大小估计,从而可以得到一般整数扩张矩阵向量细分方程所生成的小波包L_p平均大小公式。需要指出的是,我们得到的定理不仅对于1≤p≤∞时成立,同时也适用于0细分树的平均大小L_p估计从欧氏空间R~s推广到非交换的Heisenberg群上,建立Heisenberg群H~s上向量细分方程的细分树L_p范数估计,从而延拓了[22,23]中的相关结果。
The purpose of this dissertation is to investigate the subdivision tree of the general vector refinement equation whose integer expanding matrix M satisfying lim_(n→8) M~(-n) = 0. We establish the L_p norm or quasi-norm estimatesof the subdivision tree. On the basis of the L_p norm or quasinorm estimates of the subdivision tree, we obtain the L_p norm mean size formula of general (vector)wavelet packets. The L_p estimates is not only true for 1≤p≤∞, but also for 0 < p < 1. We remark here that there are very few discussions on L_p estimates for 0 < p < 1 in the literatures. We apply our conclusions established in above to multivariate quincunx biorthogonal wavelet packets and multiple biorthogonal wavelet packets in L_p. We obtain a asymptotic formula of the mean size for multivariate quincunx biorthogonalwavelet packets. Similarily, we also get a asymptotic mean size formula for multiple biorthogonal wavelet packets in L_p. These two concrete formulascontain important information for these two kinds of wavelet packets. Some results in [40,41,56] are special cases of our theorems. Furthermore, we generalize the L_p norm of the subdivision tree from Euclid space R~s to the noncommutative Heisenberg group H~s. We get the L_p norm estimates of the subdivision tree on Heisenberg group H~s, which extend the corresponding conclusions in [22,23].
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