On the convergence of the MAOR method

In order to solve a linear system Ax = b, Hadjidimos et al. (1992) defined a class of modified AOR (MAOR) method, whose special case implies the MSOR method. In this paper, some sufficient and/or necessary conditions for convergence of the MAOR and MSOR methods will be achieved, when A is a two-cyclic matrix and when A is a Hermitian positive-definite matrix, an H-, L- or M-matrix, and a strictly or irreducibly diagonally dominant matrix. The convergence results on the MSOR method are better than some known theorems. The optimum parameters and the optimum spectral radii of the MAOR and MSOR methods are obtained, which also answers the open problem given by Hadjidimos et al.     

GSOR,GAOR,GSSOR和GSAOR

M.M.Martins于1986年提出了解线性代数方程组的MSOR方法,其实这种方法就是[2]中GAOR方法的特例,而且在[2]中还讨论了GSAOR方法,收敛性条件只含Jacobi迭代矩阵的谱半径,不含方程组的系数,特别是建立了GAOR或GSAOR收敛和方程组系数A为H阵的等价性,故所得结果比较好.又[1]中的定理1也是[4]中一个     

On the convergence of the modified overrelaxation method

The modified overrelaxation (MSOR) method is applied to a linear system Ax=b, where A has property A. We get bounds for the spectral radius of the iteration matrix of this method, and we achieve convergence conditions for the MSOR method when A is strictly diagonally dominant. We extend our conclusions to another kind of matrices—H,L,M or Stieltjes. In the last section we use the vectorial norms, getting convergence conditions for the MSOR method, when A is a block-H matrix. We also generalize a theorem of Robert's for this kind of matrices.     

A note on the convergence of the MAOR method

The MAOR method as a generalization of the well-known MSOR method was introduced by Hadjidimos et al. (Appl. Numer. Math. 10 (1992) 115–127) and investigated in Y. Song (J. Comput. Appl. Math. 79 (1997) 299–317) where some convergence results for the case when matrix of the system is strictly diagonally dominant are obtained. In this paper we shall improve these results.     

An error bound for the modified successive overrelaxation method

In this paper we consider the modified successive overrelaxation(MSOR)methodto appropriate the solution of the linear system D^-1/2 Ax =D^-1/2b, where A is a symmetric, positive definite and consistentlyordered matrix and D is a diagonal matrix with the diagonalidentical to that of A. The main purpose of this paper is to obtain some theoreticalresults, namely a bound for the norm of _n = v –v_n in termsof the norms _n –v_n-1, _n+1 –v_n and their inner product,where v =D^-1/2 x and v_n is the nth iteration vector, obtainedusing the (MSOR)method.     

一类矩阵的AOR迭代收敛性分析及其与SOR迭代的比较

1 引言 许多实际问题最后常归结为解一个或一些矩阵的线性代数方程组Ax=b （1.1）这里讨论A为（1,1）相容次序矩阵的情形。     

Extrapolated alternating direction implicit iterative methods

This paper describes a new generalised (extrapolated) A.D.I. method for the solution of Laplace's equation. This method uses (i) a fixed acceleration parameter and (ii) the set of acceleration parameters of Douglas. The theory is applied to the 2-dimensional case and optimum numerical results are obtained.     

The modified alternating direction preconditioning method for the numerical solution of the elliptic self-adjoint second order and biharmonic equations

This paper introduces a generalised ADI method called the Modified Alternating Direction Preconditioning method (MADP method) for the numerical solution of the elliptic self-adjoint second order and biharmonic equations in a rectangle. The related theory of the MADP method is developed in detail and optimum values for the involved parameters are determined for both cases.     

Analysis of algebraic multigrid parameters for two-dimensional steady-state heat diffusion equations

In this work, it is provided a comparison for the algebraic multigrid (AMG) and the geometric multigrid (GMG) parameters, for Laplace and Poisson two-dimensional equations in square and triangular grids. The analyzed parameters are the number of: inner iterations in the solver, grids and unknowns. For the AMG, the effects of the grid reduction factor and the strong dependence factor in the coarse grid on the necessary CPU time are studied. For square grids the finite difference method is used, and for the triangular grids, the finite volume one. The results are obtained with the use of an adapted AMG1R6 code of Ruge and Stüben. For the AMG the following components are used: standard coarsening, standard interpolation, correction scheme (CS), lexicographic Gauss–Seidel and V-cycle. Comparative studies among the CPU time of the GMG, AMG and singlegrid are made. It was verified that: (1) the optimum inner iterations is independent of the multigrid, however it is dependent on the grid; (2) the optimum number of grids is the maximum number; (3) AMG was shown to be sensitive to both the variation of the grid reduction factor and the strong dependence factor in the coarse grid; (4) in square grids, the GMG CPU time is 20% of the AMG one.     

L1 regularization method in electrical impedance tomography by using the L1-curve (Pareto frontier curve)

Electrical impedance tomography (EIT), as an inverse problem, aims to calculate the internal conductivity distribution at the interior of an object from current-voltage measurements on its boundary. Many inverse problems are ill-posed, since the measurement data are limited and imperfect. To overcome ill-posedness in EIT, two main types of regularization techniques are widely used. One is categorized as the projection methods, such as truncated singular value decomposition (SVD or TSVD). The other categorized as penalty methods, such as Tikhonov regularization, and total variation methods. For both of these methods, a good regularization parameter should yield a fair balance between the perturbation error and regularized solution. In this paper a new method combining the least absolute shrinkage and selection operator (LASSO) and the basis pursuit denoising (BPDN) is introduced for EIT. For choosing the optimum regularization we use the L1-curve (Pareto frontier curve) which is similar to the L-curve used in optimising L2-norm problems. In the L1-curve we use the L1-norm of the solution instead of the L2 norm. The results are compared with the TSVD regularization method where the best regularization parameters are selected by observing the Picard condition and minimizing generalized cross validation (GCV) function. We show that this method yields a good regularization parameter corresponding to a regularized solution. Also, in situations where little is known about the noise level σ, it is also useful to visualize the L1-curve in order to understand the trade-offs between the norms of the residual and the solution. This method gives us a means to control the sparsity and filtering of the ill-posed EIT problem. Tracing this curve for the optimum solution can decrease the number of iterations by three times in comparison with using LASSO or BPDN separately.     

自适应多重网格法与超松弛法的比较

多重网格法(Multiple Grid Method,简称M-G方法)是近年来出现的快速方法之一,本文在M-G方法中采用自适应控制层间转换的技术,并将自适应M-G方法与G-S迭代方法及SOR迭代方,法进行了比较。其计算结果表明,自适应M-G方法的计算量比G-S迭代及SOR迭代少得多,当M-G方法所用层数为4-6层,这种优越性就更加明显,且自适应M-G方法中选取控制参数有很大的灵活性。     

预条件同时置换(PSD)迭代法的收敛性分析

1引言求解线性方程组Ax=6,(1.1)其中A∈R~(n×n)非奇异阵且对角元非零,x,b∈R~n,x未知,b已知.不失一般性,我们假设A=I-L-U,(1.2)其中L,U分别为A的严格下和上三角矩阵,相应的Jacobi迭代矩阵为B=L U.(1.3)若Q是非奇异阵且Q~(-1)易计算,于是(1.1)可以变成     

Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation

The increasing demand for high reliability, safety and availability of technical systems calls for innovative maintenance strategies. The use of prognostic health management (PHM) approach where maintenance action is taken based on current and future health state of a component or system is rapidly gaining popularity in the maintenance industry. Multiclass support vector machines (MC-SVM) has been identified as a promising algorithm in PHM applications due to its high classification accuracy. However, it requires parameter tuning for each application, with the objective of minimizing the classification error. This is a single objective optimization problem which requires the use of optimization algorithms that are capable of exhaustively searching for the global optimum parameters. This work proposes the use of hybrid differential evolution (DE) and particle swarm optimization (PSO) in optimally tuning the MC-SVM parameters. DE identifies the search limit of the parameters while PSO finds the global optimum within the search limit. The feasibility of the approach is verified using bearing run-to-failure data and the results show that the proposed method significantly increases health state classification accuracy. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

The construction of a nose shape of minimum drag for specified external dimensions and volume using Euler equations

The problem of constructing the axisymmetric nose shape which gives minimum wave drag for a specified volume and external dimensions is solved by a direct method using Euler equations. As in the Newton's formula approximation, the optimum contours together with the front faces – a segment of the boundary extremum along the longitudinal coordinate and the gently sloping segment of the bilateral extremum – may contain a cylindrical end part with a horizontal segment of the boundary extremum with respect to the maximum admissible radial coordinate. In the direct method, the required parameters (“controls”), which define the shape of the optimum contour, are the radii corresponding to the points of the segment of a bilateral extremum, including the radius of the face for fixed abscissas. For each aspect ratio (the ratio of the length to the radius of the base), when a certain value of the volume coefficient (the ratio of the volume to the volume of a cylinder of maximum external dimensions) is exceeded, the optimum nose shape is completed by a rear cylindrical part. The optimum nose shape, which begins from a certain initial contour, that satisfies the limitations of the problem, is constructed after a finite number of cycles. In each cycle, all the controls are corrected, and together with the directions of the change, their increments are found, while the information necessary for this for any number of controls is obtained after three direct calculations. One other advantage of the method is the rapid, close to quadratic, convergence. The nose shapes constructed are compared with the nose shapes that are optimum in the Newton's formula approximation.     

Classic Kriging <Emphasis Type="Italic">versus</Emphasis> Kriging with bootstrapping or conditional simulation: classic Kriging’s robust confidence intervals and optimization

Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by ‘efficient global optimization’ (EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input combination. This conclusion is based on bootstrapping and conditional simulation.     

Maximum-likelihood adjoint-state finite-element estimation of groundwater parameters under steady- and nonsteady-state conditions

A method is presented for estimating the hydraulic parameters of groundwater flow models under steady- and nonsteady-state conditions. The estimation problem is posed in the framework of maximum-likelihood theory by means of a log-likelihood criterion that includes prior estimates of the parameters. To allow for an incomplete knowledge of the covariances of the prior head and parameter errors, these covariances are expressed in terms of a few unknown statistical parameters that may be estimated jointly with the hydraulic parameters. Computational efficiency is achieved by evaluating the gradient of the estimation criterion with an adjoint-state finite-element scheme and using a combination of conjugate-gradient algorithms, coupled with Newton's method for determining the step size to be taken at each iteration. Model structure identification criteria developed in the time-series literature (all of which utilize the maximum-likelihood concept) are shown to be useful for selecting the best way to parametrize a groundwater flow region when a number of alternative schemes of parametrization are given. The paper also demonstrates the potential utility of the proposed estimation method for the optimum design of space-time measurement networks. A case study dealing with three-dimensional flow in a multiaquifer system is briefly discussed.     

Computation of Optimum Re-order Levels and Quantities for a Re-order Level Stock Control System

The purpose of this article is to present a method of calculating optimum re-order levels and quantities for a re-order level stock control system where the criterion of optimality is the minimization of total expected cost. Many solutions to this, and similar, stock control problems exist in the literature; however, most of these incorporate approximations which simplify the mathematics, but give rise to non-optimal solutions for certain values of the basic parameters. The method described here enables optimum policies to be evaluated reasonably quickly for any item for which the basic parameters can be estimated.In the article we give the mathematical formulation and solution of the problem, together with a computing routine which finds optimum policies.     

A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water

Based on the mechanism of biological DNA genetic information and evolution, a modified DNA genetic algorithm (MDNA-GA) is proposed to estimate the kinetic parameters of the 2-Chlorophenol oxidation in supercritical water. In this approach, DNA encoding method, choose crossover operator and frame-shift mutation operator inspired by the biological DNA are developed for improving the global searching ability. Besides, an adaptive mutation probability which can be adjusted automatically according to the diversity of population is also adopted. A local search method is used to explore the search space to accelerate the convergence towards global optimum. The performance of MDNA-GA in typical benchmark functions and kinetic parameter estimation is studied and compared with RNA-GA. The experimental results demonstrate that the proposed algorithm can overcome premature convergence and yield the global optimum with high efficiency.     

Optimum Modified Extrapolated Jacobi Method for Consistently Ordered Matrices

1.IlltroductionandPreliminariesWeconsiderthelineaxsystemAx=b,(1.1)wheteAERn,",bERnanddet(A)/O.WeaJ8oassumethatAhasthef0rmwhereA11,A22axesquarenonsingular(usuallydiagonal)matrices-Asisknow[61,AisaconSisentlyordered2-cyclicmatris.Forsolving(1.1)weintendtousethef0lfowngsimpleiterativemethod:In(1.4)and(1.6),w1,w2arenonzeroparameters(extraP0lati0nparameters)andI1,I2areidelltitymatricesofthesamesizesasA11anA22respectively.Theconstructionofmeth0d(1.3)isbasedonthesplittingA=M-N,whereM=Dfl-1…