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We first introduce trivariate multiresolution analysis and trivariate orthogonal wavlets. We then give a method for constructing a class of trivariate nonseparable compactly supported orthogonal scaling functions and the related wavelets. Finally, we study the regularity of these wavelets. We can get arbitrarily smooth trivariate orthogonal wavelets. [ABSTRACT FROM AUTHOR]

Abstract: A more flexible method of constructing multiwavelet packets with the scale a ≥ 2 from the same set of multiwavelets is discussed in this paper. The properties of these packets are studied. The formulae for performing iterations and decomposition are given. L 2(R) can be further decomposed by these multiwavelet packets and good bases of L 2(R) are given. Finally, we provide the algorithms for the decomposition and reconstruction using these multiwavelet packets. [Copyright &y& Elsevier]

Abstract: For many applications, it is useful to know whether or not a matrix is an H-matrix. An algorithmic characterization of H-matrices was given by [Li et al.] This method is useful, but entails estimating an optimum value for a parameter ε, that plays a crucial role in the algorithm. In Part I of this paper, the authors present a new method which eliminates the arbitrary parameter and is an equivalent algorithmic characterization of H-matrices. A few examples are given to illustrate relative merits of the two methods. In Part II, the editor describes an alternative which seems to eliminate deficiencies in methods discussed in Part I. [Copyright &y& Elsevier]