非奇异H-矩阵的实用判定

给出了几个非奇异H-矩阵的新的实用判定条件,改进了近期的相关结果,扩大了常见H-矩阵实用判定结果的使用范围.     

基于严格α-双链对角占优矩阵的非奇H-矩阵的判定准则

研究了非奇H-矩阵的判定问题.先给出了几个判定严格α-双链对角占优矩阵的充要条件,进一步利用矩阵对角占优理论得到了判定非奇H-矩阵的一些充分条件,推广和改进了已有的相关结果,并用数值算例说明了这些判定方法的有效性.     

广义H-矩阵的一组判定条件

广义H-矩阵在动力系统理论,流体动力学等领域有着广泛的应用.本文引入新参数,利用子矩阵的谱半径,给出正定条件下广义H-矩阵的一组判定方法,当块矩阵退化为点矩阵时,这些条件即为非奇异H-矩阵的充分条件.这些结果改进了近期的相关结果,并用数值算例说明本文判定条件的有效性.     

一类非奇H-矩阵的实用判据

非奇H-矩阵在数值分析和矩阵理论的研究中非常重要,但实际判定一个非奇异H-矩阵却非常困难.给出一类非奇异H-矩阵新的判定条件,改进了近期的相关结果,并用数值例子说明了结果判定范围的更广泛性.     

非奇H-矩阵的一组细分迭代判定条件

本文研究了非奇H-矩阵的细分迭代判定问题.利用细分和迭代的方法,细分了矩阵的非对角占优行集合,并且构造了递进系数,得到了非奇H-矩阵的一组细分迭代判定条件,推广和改进了已有的相关结果.数值算例说明了这些判定方法的有效性.     

广义H-矩阵的一组新判定条件

利用矩阵指标集的k-级划分和子矩阵的谱半径,给出了正定条件下广义H-矩阵的一组判定条件,当块矩阵退化为点矩阵时,这些条件即为非奇异H-矩阵的充分条件.这些结果改进了近期的相关结果,并用数值算例说明本文判定条件的有效性.     

非奇异H-矩阵的实用判定

H-矩阵在许多领域中都起着非常重要的作用,例如数学分析、矩阵理论、数学经济学、控制论等.但是在实际运用中判定H-矩阵却十分困难.本文类似于文[4],均以α-对角占优理论为基础,给出H-矩阵的若干实用判定,改进了文[3]的相应结果.     

广义H-矩阵的一组充分条件

利用矩阵的连续过渡、子矩阵的谱半径估计等方法,研究了正定条件下的广义H-矩阵的判别法．给出了判定正定条件下广义H-矩阵的几个充分条件,当块矩阵退化为点矩阵时,这些条件即为非奇异H-矩阵的充分条件．     

Ostrowski定理的推广与非奇异H-矩阵的实用判定

利用α2-双对角占优理论,给出了几个判定非奇异H-矩阵的充分条件,扩大了非奇异H-矩阵的判定范围,并给出了相应的数值算例说明结果的有效性．     

关于"非奇异H-矩阵的实用新判定"的改进研究

利用新的正对角因子,得出几个非奇异H-矩阵新的判定条件,改进和推广了"非奇异H-矩阵的实用新判定"一文的主要结果,并用数值例子说明了结论的有效性.     

An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimization

Recently, we propose a nonlinear conjugate gradient method, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the weak Wolfe conditions. In this paper, we will study methods related to the new nonlinear conjugate gradient method. Specifically, if the size of the scalar _k with respect to the one in the new method belongs to some interval, then the corresponding methods are proved to be globally convergent; otherwise, we are able to construct a convex quadratic example showing that the methods need not converge. Numerical experiments are made for two combinations of the new method and the Hestenes–Stiefel conjugate gradient method. The initial results show that, one of the hybrid methods is especially efficient for the given test problems.     

Mixed methods for fourth-order elliptic and parabolic problems using radial basis functions

By extending Wendlands meshless Galerkin methods using RBFs, we develop mixed methods for solving fourth-order elliptic and parabolic problems by using RBFs. Similar error estimates as classical mixed finite element methods are proved. AMS subject classification 35G15, 65N12     

$$\ell _p$$ Regularized low-rank approximation via iterative reweighted singular value minimization

In this paper we study the \(\ell _p\) (or Schatten-p quasi-norm) regularized low-rank approximation problems. In particular, we introduce a class of first-order stationary points for them and show that any local minimizer of these problems must be a first-order stationary point. In addition, we derive lower bounds for the nonzero singular values of the first-order stationary points and hence also of the local minimizers of these problems. The iterative reweighted singular value minimization (IRSVM) methods are then proposed to solve these problems, whose subproblems are shown to have a closed-form solution. Compared to the analogous methods for the \(\ell _p\) regularized vector minimization problems, the convergence analysis of these methods is significantly more challenging. We develop a novel approach to establishing the convergence of the IRSVM methods, which makes use of the expression of a specific solution of their subproblems and avoids the intricate issue of finding the explicit expression for the Clarke subdifferential of the objective of their subproblems. In particular, we show that any accumulation point of the sequence generated by the IRSVM methods is a first-order stationary point of the problems. Our computational results demonstrate that the IRSVM methods generally outperform the recently developed iterative reweighted least squares methods in terms of solution quality and/or speed.     

Use of EM algorithm for data reduction under sparsity assumption

Recent scientific applications produce data that are too large for storing or rendering for further statistical analysis. This motivates the construction of an optimum mechanism to choose only a subset of the available information and drawing inferences about the parent population using only the stored subset. This paper addresses the issue of estimation of parameter from such filtered data. Instead of all the observations we observe only a few chosen linear combinations of them and treat the remaining information as missing. From the observed linear combinations we try to estimate the parameter using EM based technique under the assumption that the parameter is sparse. In this paper we propose two related methods called ASREM and ESREM. The methods developed here are also used for hypothesis testing and construction of confidence interval. Similar data filtering approach already exists in signal sampling paradigm, for example, Compressive Sampling introduced by Candes et al. (Commun Pure Appl Math 59(8):1207–1223, 2006) and Donoho (IEEE Trans Inf Theory 52: 1289–1306, 2006). The methods proposed in this paper are not claimed to outperform all the available techniques of signal recovery, rather our methods are suggested as an alternative way of data compression using EM algorithm. However, we shall compare our methods to one standard algorithm, viz., robust signal recovery from noisy data using min-\(\ell _{1}\) with quadratic constraints. Finally we shall apply one of our methods to a real life dataset.     

Parallel nonlinear multisplitting methods

Summary Linear multisplitting methods are known as parallel iterative methods for solving a linear systemAx=b. We extend the idea of multisplittings to the problem of solving a nonlinear system of equationsF(x)=0. Our nonlinear multisplittings are based on several nonlinear splittings of the functionF. In a parallel computing environment, each processor would have to calculate the exact solution of an individual nonlinear system belonging to his nonlinear multisplitting and these solutions are combined to yield the next iterate. Although the individual systems are usually much less involved than the original system, the exact solutions will in general not be available. Therefore, we consider important variants where the exact solutions of the individual systems are approximated by some standard method such as Newton's method. Several methods proposed in literature may be regarded as special nonlinear multisplitting methods. As an application of our systematic approach we present a local convergence analysis of the nonlinear multisplitting methods and their variants. One result is that the local convergence of these methods is determined by an induced linear multisplitting of the Jacobian ofF.Dedicated to the memory of Peter Henrici     

Essentially Symplectic Boundary Value Methods for Linear Hamiltonian Systems

1.IlltroductiollInmanyareasofphysics,mechanics,etc.,HamiltoniansystemsofODEsplayaveryimportantrole.Suchsystemshavethefollowinggeneralform:where,bydenotingwithOfandimthenullmatrixandtheidentitymatrixofordermarespectively,SimplepropertiesofthematrixJZmarethefollowingones:Inequation(1)AH(~,t)isthegradientofascalarfunctionH(y,t),usuallycalledHamiltonian.InthecasewhereH(y,t)=H(y),thenthevalueofthisfunctionremainsconstantalongt.hesollltion7/(t),t,hatis'*ReceivedFebruaryI3,1995.l)Worksupporte…     

Superconvergence of the numerical traces of discontinuous Galerkin and Hybridized methods for convection-diffusion problems in one space dimension

In this paper, we uncover and study a new superconvergence property of a large class of finite element methods for one-dimensional convection-diffusion problems. This class includes discontinuous Galerkin methods defined in terms of numerical traces, discontinuous Petrov-Galerkin methods and hybridized mixed methods. We prove that the so-called numerical traces of both variables superconverge at all the nodes of the mesh, provided that the traces are conservative, that is, provided they are single-valued. In particular, for a local discontinuous Galerkin method, we show that the superconvergence is order when polynomials of degree at most are used. Extensive numerical results verifying our theoretical results are displayed.

     

Hybrid methods for large sparse nonlinear least squares

Hybrid methods are developed for improving the Gauss-Newton method in the case of large residual or ill-conditioned nonlinear least-square problems. These methods are used usually in a form suitable for dense problems. But some standard approaches are unsuitable, and some new possibilities appear in the sparse case. We propose efficient hybrid methods for various representations of the sparse problems. After describing the basic ideas that help deriving new hybrid methods, we are concerned with designing hybrid methods for sparse Jacobian and sparse Hessian representations of the least-square problems. The efficiency of hybrid methods is demonstrated by extensive numerical experiments.This work was supported by the Czech Republic Grant Agency, Grant 201/93/0129. The author is indebted to Jan Vlek for his comments on the first draft of this paper and to anonymous referees for many useful remarks.     

Splitting methods for tensor equations

The Jacobi, Gauss‐Seidel and successive over‐relaxation methods are well‐known basic iterative methods for solving system of linear equations. In this paper, we extend those basic methods to solve the tensor equation , where is an m th‐order n ?dimensional symmetric tensor and b is an n ‐dimensional vector. Under appropriate conditions, we show that the proposed methods are globally convergent and locally r‐linearly convergent. Taking into account the special structure of the Newton method for the problem, we propose a Newton‐Gauss‐Seidel method, which is expected to converge faster than the above methods. The proposed methods can be extended to solve a general symmetric tensor equations. Our preliminary numerical results show the effectiveness of the proposed methods.     

Second derivative general linear methods with inherent Runge–Kutta stability

In this paper, we find some relationships among the coefficients matrices of second derivative general linear methods (SGLMs) which are sufficient conditions, but not necessary, to ensure the methods have Runge–Kutta stability (RKS) property. Considering these conditions, we construct some A– and L–stable SGLMs with inherent RKS of orders up to five. Also, some numerical experiments for the constructed methods in variable stepsize environment are given.