拉格朗日展开定理的两个基本应用

The present paper offers two likely neglected applications of the classical Lagrange expansion formula.One is a unified approach to some age-old derivative identities originally due to Pfaff and Cauchy.Another is two explicit matrix inversions which may serve as common generalizations of some known inverse series relations.     

Finding off‐diagonal entries of the inverse of a large symmetric sparse matrix

The method fast inverse using nested dissection (FIND) was proposed to calculate the diagonal entries of the inverse of a large sparse symmetric matrix. In this paper, we show how the FIND algorithm can be generalized to calculate off‐diagonal entries of the inverse that correspond to ‘short’ geometric distances within the computational mesh of the original matrix. The idea is to extend the downward pass in FIND that eliminates all nodes outside of each node cluster. In our advanced downwards pass, it eliminates all nodes outside of each ‘node cluster pair’ from a subset of all node cluster pairs. The complexity depends on how far (i,j) is from the main diagonal. In the extension of the algorithm, all entries of the inverse that correspond to vertex pairs that are geometrically closer than a predefined length limit l will be calculated. More precisely, let α be the total number of nodes in a two‐dimensional square mesh. We will show that our algorithm can compute O(α^{3 ∕ 2 + 2ε}) entries of the inverse in O(α^{3 ∕ 2 + 2ε}) time where l = O(α^{1 ∕ 4 + ε}) and 0 ≤ ε ≤1 ∕ 4. Numerical examples are given to illustrate the efficiency of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

Some Features of Gaussian Elimination with Rook Pivoting

Rook pivoting is a relatively new pivoting strategy used in Gaussian elimination (GE). It can be as computationally cheap as partial pivoting and as stable as complete pivoting. This paper shows some new attractive features of rook pivoting. We first derive error bounds for the LU factors computed by GE and show rook pivoting usually gives a highly accurate U factor. Then we show accuracy of the computed solution of a linear system by rook pivoting is essentially independent of row scaling of the coefficient matrix. Thus if the matrix is ill-conditioned due to bad row scaling a highly accurate solution can usually be obtained. Finally for a typical inversion method involving the LU factorization we show rook pivoting usually makes both left and right residuals for the computed inverse of a matrix small.     

On finding robust approximate inverses for large sparse matrices

This paper presents a method based on matrix-matrix multiplication concepts for determining the approximate (sparse) inverses of sparse matrices. The suggested method is a development on the well-known Schulz iteration and it can successfully be combined with iterative solvers and sparse approximation techniques as well. A detailed discussion on the convergence rate of this scheme is furnished. Results of numerical experiments are also reported to illustrate the performance of the proposed method.     

A new way to evaluate the Probability and Fresnel integrals

In this article, we show how Laplace Transform may be used to evaluate variety of nontrivial improper integrals, including Probability and Fresnel integrals. The algorithm we have developed here to evaluate Probability, Fresnel and other similar integrals seems to be new. This method transforms the evaluation of certain improper integrals into evaluation of improper integrals of the corresponding Laplace transform, which in many cases are much easier.     

$(f,g)$-反演的一些应用

我们给出了马欣荣的关于$(f, g)$-反演的三种应用. 在$(f, g)$-演中通过取具体的函数和序列, 我们推出了一些关于超几何级数与调和数的恒等式. 然后我们给出了一些关于$q$-超几何项的反演关系. 最后, 我们将$(f, g)$-反演和$q$-微分算子结合, 得到了一些$q$-级数恒等式.     

Linear-scaling Cholesky decomposition

We present linear-scaling routines for the calculation of the Cholesky decomposition of a symmetric positive-definite matrix and its inverse. As an example, we consider the inversion of the overlap matrix of DNA and amylose fragments as well as of linear alkanes, where the largest system corresponds to a 21,442 x 21,442 matrix. The efficiency and the scaling behavior are discussed and compared to standard LAPACK routines. Our Cholesky routines are publicly available on the web.