Successive overrelaxation method with projection for finite element solutions applied to the Dirichlet problem of the nonlinear elliptic equation Δu-bu2

this study is a continuation of a previous paper [Computing 38 (1987), pp.117–132]. In this paper, we consider the successive overrelaxation method with projection for obtaining finite element solutions applied to the Dirichlet problem of the nonlinear elliptic equation $$\begin{gathered} \Delta u = bu^2 in\Omega , \hfill \\ u = g(x)on\Gamma . \hfill \\ \end{gathered} $$ . Some numerical examples are given to illustrate the effectiveness.     

SORR回归算法在胆固醇测定中的应用

借鉴分类问题的算法,推广到回归问题中去,针对用于分类问题的SOR(successive overrelaxation for support vector)支持向量机算法,提出SORR(successive overrelaxation for support vector regression)支持向量回归算法,并应用于医学上三类血浆脂蛋白(VLDL、LDL、HDL)测定样本中胆固醇的含量。数值实验表明:SORR算法有效,与标准的支持向量回归SVR算法相比,保持了相同的回归精度,提高了学习速度,为临床上测定胆固醇含量提供新的有效方法。     

Alternating splitting waveform relaxation method and its successive overrelaxation acceleration

For the large sparse implicit linear initial value problem, we present a block successive overrelaxation scheme for the alternating direction implicit waveform relaxation method to further accelerate its convergence speed, and discuss the convergence property of the resulting iteration method in detail. Numerical implementations about several non-Hermitian implicit linear initial value problems show that the alternating direction implicit waveform relaxation method is very effective, and the block successive overrelaxation technique really accelerates its convergence speed.     

A modified symmetric successive overrelaxation method for augmented systems

In this paper, we establish a modified symmetric successive overrelaxation (MSSOR) method, to solve augmented systems of linear equations, which uses two relaxation parameters. This method is an extension of the symmetric SOR (SSOR) iterative method. The convergence of the MSSOR method for augmented systems is studied. Numerical examples show that the new method is an efficient method.     

GSOR Method for the Equality Constrained Least Squares Problems and the Generalized Least Squares Problems

In a recent paper [4], Li et al . gave a generalized successive overrelaxation (GSOR) method for the least squares problems. In this paper, we show that the GSOR method can be applied to the equality constrained least squares (LSE) problems and the generalized least squares (GLS) problems.     

Chebyshev polynomial acceleration for block SOR methods for solving the rank-deficient least-squares problem

In this article, we give the acceleration of the block successive overrelaxation (SOR) method for solving the rank-deficient least-squares problem. Santos and Silva proposed the two-block SOR method and the three-block SOR method. Here, we consider the acceleration of the two-block SOR method and the three-block SOR method using the Chebyshev polynomial and derive what we term the C-2-block SOR method and the C-3-block SOR method. The advantage of our methods is that we can get good results with very small iteration number. The comparison between the C-2-block method and the C-3-block method is presented. Finally, numerical examples are given.     

The determination of optimum accelerating factors for successive overrelaxation on an equidistant and non-equidistant rectangular net

A modification to the successive overrelaxation iterative procedure for solving elliptic partial differential equations is presented. The modified method is based on an extension of Brazier's nodal overrelaxation method in one dimension, characterised by the use of a different overrelaxation factor for each point in the net. The extension to several dimensions make use of the separability of the variables for the error distribution. Thus the optimum one dimensional results are directly used in the several dimensional problem.The present method has been examined in one and two dimensions, for equidistant and non-equidistant nets. The computational time required to obtain a given accuracy for a solution was found for all two dimensional cases to be half (or less) of that required by conventional methods.     

广义修正HSS迭代法的超松弛加逮

通过推广修正埃尔米特和反埃尔米特（MHSS）迭代法,我们进一步得到了求解大型稀疏非埃尔米特正定线性方程组的广义MHSS（GMHSS）迭代法．基于不动点方程,我们还将超松弛（SOR）技术运用到了GMHSS迭代法,得到了关于GMHSS迭代法的SOR加速,并分析了它的收敛性．数值算例表明,SOR技术能够大大提高加速GMHSS迭代法的收敛效率．     

Generalized successive overrelaxation iterative method for a class of complex symmetric linear system of equations

In this paper, to solve a broad class of complex symmetric linear systems, we recast the complex system in a real formulation and apply the generalized successive overrelaxation (GSOR) iterative method to the equivalent real system. We then investigate its convergence properties and determine its optimal iteration parameter as well as its corresponding optimal convergence factor. In addition, the resulting GSOR preconditioner is used to precondition Krylov subspace methods such as the generalized minimal residual method for solving the real equivalent formulation of the system. Finally, we give some numerical experiments to validate the theoretical results and compare the performance of the GSOR method with the modified Hermitian and skew-Hermitian splitting iteration.     

USSOR methods for solving the rank deficient linear least squares problem

In order to find the least squares solution of minimal norm to linear system \(Ax=b\) with \(A \in \mathcal{C}^{m \times n}\) being a matrix of rank \(r< n \le m\), \(b \in \mathcal{C}^{m}\), Zheng and Wang (Appl Math Comput 169:1305–1323, 2005) proposed a class of symmetric successive overrelaxation (SSOR) methods, which is based on augmenting system to a block \(4 \times 4\) consistent system. In this paper, we construct the unsymmetric successive overrelaxation (USSOR) method. The semiconvergence of the USSOR method is discussed. Numerical experiments illustrate that the number of iterations and CPU time for the USSOR method with the appropriate parameters is respectively less and faster than the SSOR method with optimal parameters.     

连续超松弛支持向量机回归算法应用研究

支持向量回归问题的研究,对函数拟合(回归逼近)具有重要的理论和应用意义.借鉴分类问题的有效算法,将其推广到回归问题中来,针对用于分类问题的SOR支持向量机有效算法,提出了SORR支持向量回归算法.在若干不同维数的数据集上,对SORR算法、ASVR算法和LibSVM算法进行数值试验,并进行比较分析.数值实验结果表明,SORR算法是有效的,与当前流行的支持向量机回归算法相比,在回归精度和学习速度上都有一定的优势.     

The eigenvalue problem (A − λB)x = 0 for symmetric matrices of high order

Vibrational problems of complex structures treated by the method of finite elements lead to the general eigenvalue problem $\text{(A ? λB)x = 0}$, where $\text{A}$ and $\text{B}$ are symmetric and sparse matrices of high order. The smallest eigenvalues and corresponding eigenvectors of interest are usually computed by a variant of the inverse vector iteration. Instead of this, the smallest eigenvalue can be computed as the minimum of the corresponding Rayleigh quotient for instance by the method of the coordinate relaxation of Faddejew/Faddejewa. The slow convergence of this simple algorithm can however be sped up considerably in analogy to the successive overrelaxation method by a systematic overrelaxation. Numerical experiments indicate indeed a rate of convergence of this coordinate overrelaxation as a function of the relaxation parameter which is comparable to that of the usual seccessive overrelaxation for linear equations. In comparison with known procedures for the solution of the general eigenvalue problem there result some important computational advantages with regard to the amount of work. Finally, the higher eigenvalues can be computed successively by minimizing the Rayleigh quotient of a modified eigenvalue problem based on a deflation process.     

拟线性对称超松弛形两步递推辨识算法

本文提出一种线性系统拟线性对称超松弛两步递推辨识新算法，并对其收敛性应用常微分方程的方法进行了分析。仿真结果表明算法和本文的收敛定理是一致的。     

Preconditioned Conjugate Gradient Method and Generalized Successive Overrelaxation Method for the Least Squares Problems

In a recent paper [4], Li et al . gave a generalized successive overrelaxation (GSOR) method for the least squares problems. In this paper, the connection between the GSOR method and the preconditioned conjugate gradient (PCG) method for the normal equations is investigated. It is shown that the PCG method is at least as fast as the GSOR method. Numerical examples demonstrates that the PCG method is much faster than the GSOR method.     

Global convergence of nonlinear successive overrelaxation via linear theory

The global convergence of nonlinear successive overrelaxation is established by utilising estimates from linear SOR theory.     

Augmented Lagrangian method applied to American option pricing

The American option pricing problem is originally formulated as a stochastic optimal stopping time problem. It is also equivalent to a variational inequality problem or a complementarity problem involving the Black-Scholes partial differential operator. In this paper, the corresponding variational inequality problem is discretized by using a fitted finite volume method. Based on the discretized form, an algorithm is developed by applying augmented Lagrangian method (ALM) to the valuation of the American option. Convergence properties of ALM are considered. By empirical numerical experiments, we conclude that ALM is more effective than penalty method and Lagrangian method, and comparable with the projected successive overrelaxation method (PSOR). Furthermore, numerical results show that ALM is more robust in terms of computation time under changes in market parameters: interest rate and volatility.     

Convergence of the interval and point TOR method

In this paper, we present the interval version of the two parameter overrelaxation iterative (TOR) method and we obtain some convergence conditions when the matrix A of the linear system Ax?=?b belongs to some classes of matrice. Similar conditions were obtained for the point TOR method.

Some results for the accelerated overrelaxation interval and point iterative (AOR) method were also obtained, which coincides with those given by Martins in Ref. [7].     

Analyse numerique du champ magnetique d'un alternateur par elements finis et sur-relaxation ponctuelle non lineaire

The purpose of this paper is to study a method of solution of the two-dimensional non-linear partial differential equation describing the magnetic state in the cross-section of an alternator.The method used is based on the combination of finite element technique with various non-linear successive point overrelaxation algorithms. We obtain a method which is very robust and efficient and gives the possibility of solving easily, even on middle-size computers, the previous problem on complicated geometries. Convergence proofs for the finite element approximation and iterative algorithms are given.     

The convergence factor of the parallel Schwarz overrelaxation method for linear systems

After introducing the parallel Schwarz overrelaxation method for linear systems, we analyse the convergence factor of the method in detail. The optimal overrelaxation parameter ω of the method is discussed in this paper. Some examples are also shown.     

Preconditioned accelerated generalized successive overrelaxation method for solving complex symmetric linear systems

In this paper, by adopting the preconditioned technique for the accelerated generalized successive overrelaxation method (AGSOR) proposed by Edalatpour et al. (2015), we establish the preconditioned AGSOR (PAGSOR) iteration method for solving a class of complex symmetric linear systems. The convergence conditions, optimal iteration parameters and corresponding optimal convergence factor of the PAGSOR iteration method are determined. Besides, we prove that the spectral radius of the PAGSOR iteration method is smaller than that of the AGSOR one under proper restrictions, and its optimal convergence factor is smaller than that of the preconditioned symmetric block triangular splitting (PSBTS) one put forward by Zhang et al. (2018) recently. The spectral properties of the preconditioned PAGSOR matrix are also proposed. Numerical experiments illustrate the correctness of the theories and the effectiveness of the proposed iteration method and the preconditioner for the generalized minimal residual (GMRES) method.