Multifrontal QR Factorization in a Multiprocessor Environment

We describe the design and implementation of a parallel QR decomposition algorithm for a large sparse matrix A . The algorithm is based on the multifrontal approach and makes use of Householder transformations. The tasks are distributed among processors according to an assembly tree which is built from the symbolic factorization of the matrix A ^T A . We first address uniprocessor issues and then discuss the multiprocessor implementation of the method. We consider the parallelization of both the factorization phase and the solve phase. We use relaxation of the sparsity structure of both the original matrix and the frontal matrices to improve the performance. We show that, in this case, the use of Level 3 BLAS can lead to very significant gains in performance. We use the eight processor Alliant˜FX/80 at CERFACS to illustrate our discussion.     

超立方机上一阶线性递推问题的并行求解

本文考察解一阶线性递推问题的三个并行算法在超立方机上的实现方案.通过给出原始数据与超立方机之间的适当的映射以及算法执行过程中数据的通讯方式,奇偶消元法、奇偶约简法和变间距二分法都可以在起立方机上有效地执行.文中给出了这些算法的复杂性结论.最后,为进一步减少通讯费用,我们提供了一种复杂性更小的新算法,其通讯时间是与问题规模无关的常数.     

A Linear Least-Squares MFS for Certain Elliptic Problems

The Method of Fundamental Solutions (MFS) is a boundary-type meshless method for the solution of certain elliptic boundary value problems. In this work, we propose an efficient algorithm for the linear least-squares version of the MFS, when applied to the Dirichlet problem for certain second order elliptic equations in a disk. Various aspects of the method are discussed and a comparison with the standard MFS is carried out. Numerical results are presented.     

Adaptive Wavelet Methods for Linear and Nonlinear Least-Squares Problems

The adaptive wavelet Galerkin method for solving linear, elliptic operator equations introduced by Cohen et al. (Math Comp 70:27–75, 2001) is extended to nonlinear equations and is shown to converge with optimal rates without coarsening. Moreover, when an appropriate scheme is available for the approximate evaluation of residuals, the method is shown to have asymptotically optimal computational complexity. The application of this method to solving least-squares formulations of operator equations $G(u)=0$ , where $G:H \rightarrow K'$ , is studied. For formulations of partial differential equations as first-order least-squares systems, a valid approximate residual evaluation is developed that is easy to implement and quantitatively efficient.     

On the Extrema of Linear Least-Squares Problems

We use the concept of generalized inverses to show that the extrema of the function are minima, A being a rectangular matrix not necessarily of full rank     

Backward Stability Analysis of Weighted Linear Least-Squares Problems

A provably backward stable algorithm for the solution of weighted linear least-squares problems with indefinite diagonal weighted matrices is presented. However, a similar algorithm is not necessarily backward stable, when the weighted matrices are generalized saddle-point matrices. Thus, conditions are derived under which the algorithm is provably backward stable.     

线性流形上反对称正交对称矩阵反问题的最小二乘解

该文讨论了线性流形上矩阵方程AX=B反对称正交对称反问题的最小二乘解及其最佳逼近问题．给出了最小二乘问题解集合的表达式，得到了给定矩阵的最佳逼近问题的解，最后给出计算任意矩阵的最佳逼近解的数值方法及算例．     

线性流形上反对称正交对称矩阵反问题的最小二乘解

该文讨论了线性流形上矩阵方程AX=B反对称正交对称反问题的最小二乘解及其最佳逼近问题. 给出了最小二乘问题解集合的表达式, 得到了给定矩阵的最佳逼近问题的解, 最后给出计算任意矩阵的最佳逼近解的数值方法及算例.     

Adaptive Algorithm for Constrained Least-Squares Problems

This paper is concerned with the implementation and testing of an algorithm for solving constrained least-squares problems. The algorithm is an adaptation to the least-squares case of sequential quadratic programming (SQP) trust-region methods for solving general constrained optimization problems. At each iteration, our local quadratic subproblem includes the use of the Gauss–Newton approximation but also encompasses a structured secant approximation along with tests of when to use this approximation. This method has been tested on a selection of standard problems. The results indicate that, for least-squares problems, the approach taken here is a viable alternative to standard general optimization methods such as the Byrd–Omojokun trust-region method and the Powell damped BFGS line search method.     

Computing Row and Column Counts for Sparse QR and LU Factorization

We present algorithms to determine the number of nonzeros in each row and column of the factors of a sparse matrix, for both the QR factorization and the LU factorization with partial pivoting. The algorithms use only the nonzero structure of the input matrix, and run in time nearly linear in the number of nonzeros in that matrix. They may be used to set up data structures or schedule parallel operations in advance of the numerical factorization.The row and column counts we compute are upper bounds on the actual counts. If the input matrix is strong Hall and there is no coincidental numerical cancellation, the counts are exact for QR factorization and are the tightest bounds possible for LU factorization.These algorithms are based on our earlier work on computing row and column counts for sparse Cholesky factorization, plus an efficient method to compute the column elimination tree of a sparse matrix without explicitly forming the product of the matrix and its transpose.This revised version was published online in October 2005 with corrections to the Cover Date.     

基于最小二乘QR算法的质心频率下移法应用研究

时间域振幅比法层析成像利用直达波的振幅变化来反演介质的衰减系数,具有简易快速等优点,在单一地质构造勘察中极具优势,但该方法易受几何扩散、仪器响应、天线耦合以及辐射模式等因素的干扰,导致成像结果失真,难以适应复杂地质构造的高精度成像.为解决该问题,提出质心频率下移法,从频率域分析振幅谱与衰减系数的关系,在此基础上实现衰减层析成像,此外,采用正则化least-squares QR (LSQR)分解算法求解层析成像方程组,提高计算速度和数值稳定性.数值模型算例和实测算例的结果表明,电磁波通过衰减介质时质心频率向主频方向减小,质心频率下移法具有较高成像精度.     

多处理机调度问题的差分算法

将Karmarkar和Karp关于数的划分问题的差分算法推广到多处理机调度问题,并通过统计检验的结果表明,这种差分算法在通常情形下具有比较好的平均性能。     

用2-块AOR方法求解最小二乘问题的收敛域

In this paper , we consided the 2-block AOR method for solving large sparse least-squares problems, and gave the convergence domains of its by the functional relation (λ-ω-1)²=ωλrμ².     

A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems

In this paper, we develop, analyze, and test a new algorithm for nonlinear least-squares problems. The algorithm uses a BFGS update of the Gauss-Newton Hessian when some heuristics indicate that the Gauss-Newton method may not make a good step. Some important elements are that the secant or quasi-Newton equations considered are not the obvious ones, and the method does not build up a Hessian approximation over several steps. The algorithm can be implemented easily as a modification of any Gauss-Newton code, and it seems to be useful for large residual problems.     

Multidomain Legendre-Galerkin Least-Squares Method for Linear Differential Equations with Variable Coefficients

The multidomain Legendre-Galerkin least-squares method is developed for solving linear differential problems with variable coefficients. By introducing a flux, the original differential equation is rewritten into an equivalent first-order system, and the Legendre Galerkin is applied to the discrete form of the corresponding least squares function. The proposed scheme is based on the Legendre-Galerkin method, and the Legendre/Chebyshev-Gauss-Lobatto collocation method is used to deal with the variable coefficients and the right hand side terms. The coercivity and continuity of the method are proved and the optimal error estimate in $H^1$-norm is obtained. Numerical examples are given to validate the efficiency and spectral accuracy of our scheme. Our scheme is also applied to the numerical solutions of the parabolic problems with discontinuous coefficients and the two-dimensional elliptic problems with piecewise constant coefficients, respectively.     

Factorization Representations in the Boundary Crossing Problems for Random Walks on a Markov Chain

Let τ be some stopping time for a random walk S _n defined on transitions of a finite Markov chain and let τ(t) be the first passage time across the level t which occurs after τ. We prove a theorem that establishes a connection between the dual Laplace-Stieltjes transforms of the joint distributions of (τ, S _τ) and (τ(t), S _τ(t)). This result applies to the study of the number of crossings of a strip by sample paths of a random walk.Original Russian Text Copyright © 2005 Lotov V. I. and Orlova N. G.The authors were partially supported by the Russian Foundation for Basic Research (Grant 05-01-00810) and the Grant Council of the President of the Russian Federation (Grant NSh-2139.2003.1).__________Translated from Sibirskii Matematicheskii Zhurnal, Vol. 46, No. 4, pp. 833–840, July–August, 2005.     

A Truncated Nonmonotone Gauss-Newton Method for Large-Scale Nonlinear Least-Squares Problems

In this paper, a Gauss-Newton method is proposed for the solution of large-scale nonlinear least-squares problems, by introducing a truncation strategy in the method presented in [9]. First, sufficient conditions are established for ensuring the convergence of an iterative method employing a truncation scheme for computing the search direction, as approximate solution of a Gauss-Newton type equation. Then, a specific truncated Gauss-Newton algorithm is described, whose global convergence is ensured under standard assumptions, together with the superlinear convergence rate in the zero-residual case. The results of a computational experimentation on a set of standard test problems are reported.     

On an Elliptical Trust-Region Procedure for Ill-Posed Nonlinear Least-Squares Problems

In this paper, we address the stable numerical solution of ill-posed nonlinear least-squares problems with small residual. We propose an elliptical trust-region reformulation of a Levenberg–Marquardt procedure. Thanks to an appropriate choice of the trust-region radius, the proposed procedure guarantees an automatic choice of the free regularization parameters that, together with a suitable stopping criterion, ensures regularizing properties to the method. Specifically, the proposed procedure generates a sequence that even in case of noisy data has the potential to approach a solution of the unperturbed problem. The case of constrained problems is considered, too. The effectiveness of the procedure is shown on several examples of ill-posed least-squares problems.     

线性流形上的广义反射矩阵反问题

设 R∈C^m×m 及 S∈C^n×n 是非平凡Hermitian酉矩阵, 即 R^H=R=R^-1≠±I_m ,S^H=S=S^-1≠±I_n.若矩阵 A∈C^m×n 满足 RAS=A, 则称矩阵 A 为广义反射矩阵.该文考虑线性流形上的广义反射矩阵反问题及相应的最佳逼近问题.给出了反问题解的一般表示, 得到了线性流形上矩阵方程AX₂=Z₂, Y₂^H A=W₂^H 具有广义反射矩阵解的充分必要条件, 导出了最佳逼近问题唯一解的显式表示.