Runge–Kutta methods for first-order periodic boundary value differential equations with piecewise constant arguments

In this paper we deal with the numerical solutions of Runge–Kutta methods for first-order periodic boundary value differential equations with piecewise constant arguments. The numerical solution is given by the numerical Green’s function. It is shown that Runge–Kutta methods preserve their original order for first-order periodic boundary value differential equations with piecewise constant arguments. We give the conditions under which the numerical solutions preserve some properties of the analytic solutions, e.g., uniqueness and comparison theorems. Finally, some experiments are given to illustrate our results.     

An eigen-based high-order expansion basis for structured spectral elements

We present an eigen-based high-order expansion basis for the spectral element approach with structured elements. The new basis exhibits a numerical efficiency significantly superior, in terms of the conditioning of coefficient matrices and the number of iterations to convergence for the conjugate gradient solver, to the commonly-used Jacobi polynomial-based expansion basis. This basis results in extremely sparse mass matrices, and it is very amenable to the diagonal preconditioning. Ample numerical experiments demonstrate that with the new basis and a simple diagonal preconditioner the number of conjugate gradient iterations to convergence has essentially no dependence or only a very weak dependence on the element order. The expansion bases are constructed by a tensor product of a set of special one-dimensional (1D) basis functions. The 1D interior modes are constructed such that the interior mass and stiffness matrices are simultaneously diagonal and have identical condition numbers. The 1D vertex modes are constructed to be orthogonal to all the interior modes. The performance of the new basis has been investigated and compared with other expansion bases.     

Systematic Study of Selected Diagonalization Methods for Configuration Interaction Matrices

Several modifications to the Davidson algorithm are systematically explored to establish their performance for an assortment of configuration interaction (CI) computations. The combination of a generalized Davidson method, a periodic two‐vector subspace collapse, and a blocked Davidson approach for multiple roots is determined to retain the convergence characteristics of the full subspace method. This approach permits the efficient computation of wave functions for large‐scale CI matrices by eliminating the need to ever store more than three expansion vectors ( b _i) and associated matrix‐vector products ( σ _i), thereby dramatically reducing the I/O requirements relative to the full subspace scheme. The minimal‐storage, single‐vector method of Olsen is found to be a reasonable alternative for obtaining energies of well‐behaved systems to within μE_h accuracy, although it typically requires around 50% more iterations and at times is too inefficient to yield high accuracy (ca. 10^?10 E_h) for very large CI problems. Several approximations to the diagonal elements of the CI Hamiltonian matrix are found to allow simple on‐the‐fly computation of the preconditioning matrix, to maintain the spin symmetry of the determinant‐based wave function, and to preserve the convergence characteristics of the diagonalization procedure. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1574–1589, 2001     

Efficient removal of unwanted signals in NMR spectra using the filter diagonalization method

It is often desirable to selectively remove corrupting or uninteresting signals from complex NMR spectra without disturbing overlapping or nearby signals. For biofluids in particular, removal of solvent and urea signals is important for retaining quantitative accuracy in NMR‐based metabonomics. This article presents a novel algorithm for efficient filtering of unwanted signals using the filter diagonalization method (FDM). Unwanted signals are modeled in the time domain using FDM. This modeled signal is subtracted from the original free induction decay. The resulting corrected signal is then processed using established workflow. The algorithm is found to be reliable and fast. By eliminating large, broad, uninteresting signals, many spectra can be subjected to fully automated absolute value processing, allowing objective preparation of spectra for pattern recognition analysis. Copyright © 2009 John Wiley & Sons, Ltd.     

三维各向异性耦合谐振子哈密顿量的对角化

根据物理量的可测实在性,应用二次型理论,一般地解决了3个质量与3个频率均不相同、坐标和动量各自具有全耦合谐振子系统的哈密顿量的可对角化问题,并具体给出了哈密顿量对角化的标准形.     

核类似铜酸盐超导模型的su_s(1,1)su_d(1,1)代数结构

由Iachello提出的核类似铜酸盐超导模型具有su(3)代数结构,其平均场近似下的Hamilton量可以写成su(3)生成元的线性组合.通过代数生成元的实现,该模型约化的Hamilton量具有Su_s(1,1)SU_d(1,1)代数结构,利用相干算子U(θ,φ)的幺正变换,可得到系统约化Hamilton量的能隙方程及本征值,发现应用不同代数结构求解会得到不同侧重点的分析结果.     

矩阵广义对角化的探讨

利用子空间关于矩阵的最小多项式研究了矩阵可广义对角化的充要条件,给出了矩阵可广义对角化的一种算法.     

关于一类二阶中立型偏微分方程的振动性的注记

获得了一类二阶中立型偏微分方程的振动性的充分性条件,所得结果推广了相应结论.     

Spectral reduction for control systems modeling passive integrated circuits

Linear control systems modeling passive integrated circuits are examined. A new algebraic method of spectral reduction equipped with efficient tools for preserving passivity is proposed and justified. For RC networks (circuits), this method is similar to and can be regarded as an extension of the well-known PACT method, which is based on congruence transforms. Up to now, such an extension seemed to be impossible, and different techniques were used for the reduction of RCL and RCLM networks. Some numerical results are presented.     

Some properties of invariant polynomials with matrix arguments and their applications in econometrics

Summary Further properties are derived for a class of invariant polynomials with several matrix arguments which extend the zonal polynomials. Generalized Laguerre polynomials are defined, and used to obtain expansions of the sum of independent noncentral Wishart matrices and an associated generalized regression coefficient matrix. The latter includes thek-class estimator in econometrics.