Blind Deconvolution Method Based on Precondition Conjugate Gradients

In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.

High-efciency improved symmetric successive over-relaxation preconditioned conjugate gradient method for solving large-scale finite element linear equations

Fast solving large-scale linear equations in the finite element analysis is a classical subject in computational mechanics. It is a key technique in computer aided engineering （CAE） and computer aided manufacturing （CAM）. This paper presents a high-efficiency improved symmetric successive over-relaxation （ISSOR） preconditioned conjugate gradient （PCG） method, which maintains lelism consistent with the original form. Ideally, the by 50% as compared with the original algorithm. the convergence and inherent paralcomputation can It is suitable for be reduced nearly high-performance computing with its inherent basic high-efficiency operations. By comparing with the numerical results, it is shown that the proposed method has the best performance.

IMPROVED PRECONDITIONED CONJUGATE GRADIENT METHOD AND ITS APPLICATION IN F.E.A.FOR ENGINEERING

In this paper two theorems with theoretical and practical significance are given in respect to the preconditioned conjugate gradient method (PCCG). The theorems discuss respectively the qualitative property of the iterative solution and the construction principle of the iterative matrix. The authors put forward a new incompletely LU factorizing technique for non-M-matrix and the method of constructing the iterative matrix. This improved PCCG is used to calculate the ill-conditioned problems and large-scale three-dimensional finite element problems, and simultaneously contrasted with other methods. The abnormal phenomenon is analyzed when PCCG is used to solve the system of ill-conditioned equations, ft is shown that the method proposed in this paper is quite effective in solving the system of large-scale finite element equations and the system of ill-conditioned equations.

The preconditioned conjugate gradient deconvolution method and its application

The preconditioned conjugate gradient deconvolution method combines the realization of sparse deconvolution and the optimal preconditioned conjugate gradient method to invert to reflection coefficients. This method can enhance the frequency of seismic data processing and widen the valid frequency bandwidth. Considering the time-varying nature of seismic signals, we replace the constant wavelet with a multi-scale time-varying wavelet during deconvolution. Numerical tests show that this method can obtain good application results.

Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations

Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term.A regularized convex term can usually preserve the image edges well in the restored image.In this paper,we consider a class of convex and edge-preserving regularization functions,i.e.,multiplicative half-quadratic regularizations,and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations.At each Newton iterate,the preconditioned conjugate gradient method,incorporated with a constraint preconditioner,is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix. The eigenvalue bounds of the preconditioned matrix are deliberately derived,which can be used to estimate the convergence speed of the preconditioned conjugate gradient method.We use experimental results to demonstrate that this new approach is efficient, and the effect of image restoration is reasonably well.

THE RESTRICTIVELY PRECONDITIONED CONJUGATE GRADIENT METHODS ON NORMAL RESIDUAL FOR BLOCK TWO-BY-TWO LINEAR SYSTEMS

The restrictively preconditioned conjugate gradient （RPCG） method is further developed to solve large sparse system of linear equations of a block two-by-two structure. The basic idea of this new approach is that we apply the RPCG method to the normal-residual equation of the block two-by-two linear system and construct each required approximate matrix by making use of the incomplete orthogonal factorization of the involved matrix blocks. Numerical experiments show that the new method, called the restrictively preconditioned conjugate gradient on normal residual （RPCGNR）, is more robust and effective than either the known RPCG method or the standard conjugate gradient on normal residual （CGNR） method when being used for solving the large sparse saddle point problems.

基于JPCG算法的真空灭弧室三维电场有限元计算

建立了高压真空灭弧室三维电场有限元计算的模型及物理方程,分析了适用于大型稀疏矩阵求解的雅可比共轭梯度算法 JPCG,给出 JPCG 算法的迭代流程。采用有限元法对高压真空灭弧室的三维电场分布进行了计算,同时应用 JPCG 算法来求解所得到的大型有限元方程组。最后采用不同的算法对真空灭弧室的自电容进行了对比计算,计算结果表明,采用 JPCG 算法可以明显减少有限元方程组求解的迭代次数,加快收敛速度,特别适合应用于三维电磁场有限元分析形成的大型稀疏方程组的求解,是用来计算大型有限元方程组的一种非常有效的方法。同时,真空灭弧室的自电容计算可以给真空灭弧室的优化设计提供参考。

A Preconditioned Conjugate Gradient Method with Active Set Strategy for1-Regularized Least Squares

In the paper,we consider the l_(1)-regularized least square problem which has been intensively involved in the fields of signal processing,compressive sensing,linear inverse problems and statistical inference.The considered problem has been proved recently to be equivalent to a nonnegatively constrained quadratic programming(QP).In this paper,we use a recently developed active conjugate gradient method to solve the resulting QP problem.To improve the algorithm’s performance,we design a subspace exact steplength as well as a precondition technique.The performance comparisons illustrate that the proposed algorithm is competitive and even performs little better than several state-of-the-art algorithms.

A Two-Level Preconditioned Conjugate-Gradient Method in Distorted and Structured Grids

In this paper,we propose a new two-level preconditioned C-G method which uses the quadratic smoothing and the linear correction in distorted but topologically structured grid.The CPU time of this method is less than that of the multigrid preconditioned C-G method(MGCG)using the quadratic element,but their accuracy is almost the same.Numerical experiments and eigenvalue analysis are given and the results show that the proposed two-level preconditioned method is efficient.

PRECONDITIONED CONJUGATE GRADIENT METHODS FOR INTEGRAL EQUATIONS OF THE SECOND KIND DEFINED ON THE HALF-LINE

We consider solving integral equations of the second kind defined on the half-line [0, infinity) by the preconditioned conjugate gradient method. Convergence is known to be slow due to the non-compactness of the associated integral operator. In this paper, we construct two different circulant integral operators to be used as preconditioners for the method to speed up its convergence rate. We prove that if the given integral operator is close to a convolution-type integral operator, then the preconditioned systems will have spectrum clustered around 1 and hence the preconditioned conjugate gradient method will converge superlinearly. Numerical examples are given to illustrate the fast convergence.

MPCG算法在GIS三相共罐式SF_6高压断路器电场计算中的应用

针对实际的GIS三相共罐式SF6高压断路器模型结构复杂的特点,分析了适用于计算多节点、多单元的大型稀疏正定实对称矩阵求解的修正预优共轭梯度算法MPCG,给出MPCG算法的迭代流程。根据系数矩阵稀疏、对称的特点,对系数矩阵的寻址方法进行了研究,引入了新的数据结构,该方法可以快速、有效地解决系数矩阵的寻址问题,占用内存少,寻址速度快。建立计算三维电场有限元模型及数理方程,根据该模型方程对高压断路器在工频测试电压下的电位和电场进行了计算,采用MPCG算法来求解所得到的大型有限元方程组。根据计算的电位和电场分布剖面图以及俯视图,可以为GIS断路器内部各部件的优化配置提供很好的参考。

应用自动微分的Newton-PCG算法(英文)

一类新的使用符号微分的Newton-PCG型算法在文献[1]和[2]被导出来了.本文建立和研究应用自动微分的相应的Newton-PCG算法,理论分析和数值实验结果显示应用自动微分之后,目标函数的维数或复杂性越大,Newton-PCG算法对Newton法的改进越显著.

机群环境下基于PCG法的有限元并行算法

在机群系统下进行有限元并行算法的研究是工程领域的前沿课题之一。文章讨论了将有限元产生的线性方程组在机群环境下使用预处理共轭梯度算法求解,由于其与有限元法的最小化过程非常相似,因此在大规模并行结构中能被有效实现。

一个Newton-PCG型算法和它的效率分析

大量的数值实验表明Newton-PCG型算法很有效,但缺乏理论上的保证.最近在文[7]中,从理论上证明了该类算法比Newton法有效.本文取消了文[7]中的过强的假设条件,在标准假设下得到了一个更有效的算法.

自共轭椭圆偏微分方程的m-step Jacobi PCG方法

M stepJacobi预处理共轭梯度法被用于求解源于自共轭椭圆偏微分方程的有限元或有限差分逼近的大型稀疏线性系统。这种方法的应用基础是相应的Jacobi迭代收敛。研究结果表明:偶数步的Jacobi预处理共轭梯度法较相邻奇数步的Jacobi预处理共轭梯度法更有效,步数越多,收敛速度越快。

用MoM-PCG-FFT分析金属栅有限阵列的散射问题

本文用矩量法、预条件共轭梯度法和快速傅里叶变换 (Mo M- PCG- FFT)的混合技术来分析金属栅有限阵列的电磁散射问题。首先以等效电流作为未知函数建立积分方程组或积 -微分方程组 ,再用矩量法 (脉冲 /点匹配 )获得一个线性代数方程组 ,其系数矩阵是一个对称二重复Toeplitz矩阵。基于这一特点 ,应用预条件共轭梯度法和快速傅里叶变换的结合算法 (PCGFFT)来求解这个线性代数方程组 ,其中预条件器选用 T.Chan循环预条件器的二重分块形式。文中给出的数值算例表明该混合技术是有效的 ,适用于较大的金属栅有限阵列的分析。

基于PVM的m-Step Jacobi PCG方法网上并行求解有限元方程

针对基于PVM的桌面PC机联网而成的网络并行计算环境中,处理机的运算速度较快而处理机间的通信相对较慢,以及微机的内存有限的实际情况,从实用的角度出发,给出了基于PVM的网上求解有限元方程组的并行m-Step Jacob i PCG方法,该算法的矩阵和向量采用行元素相邻单元贡献法实现有限元总体刚度矩阵和荷载向量的并行计算与组装,分块储存在各处理机上,其处理机间通信较少。并在1-4台桌面PC机连接成的局域网,PVM3.4 on W indow2000,VC 6.0并行计算平台上编程对该算法进行了数值试验,得到了较理想的结果。

基于SOR-PCG的低复杂度信号检测算法研究

针对最小均方误差信号检测算法复杂度随着天线数量增加呈指数增长的问题,提出低复杂度的预处理共轭梯度信号检测算法。该算法通过灵活调整松弛因子,运用预处理技术降低矩阵条件数,从而加快共轭梯度信号检测算法的收敛速度。仿真结果显示,该算法在小数量的迭代中能够达到和最小均方误差检测算法相似的误码率,算法复杂度下降了一个数量级。通过选择适当的松弛因子,相比直接用共轭梯度法,能够更快收敛到最佳值。

并行PCG算法在电法勘探中的应用研究

采用有限元法进行电法勘探时,会产生大型稀疏线性方程组,如何提高方程组的求解效率成为物探研究的关键。针对传统直接法难以实现并行求解的缺点,提出了在Beowulf集群环境下,采用并行PCG算法求解物探系统线性方程组。在集群环境下,该算法具有机器间相互通讯少、时间复杂度低等优点,并且易于并行实现。实验结果表明,采用PCG算法获得了良好的并行效果。

采用高阶近似逆矩阵的块PCG法

本文应用矩阵元素阶、阶矩阵及消去法的影响域等概念,给出了强主元多对角阵高阶近似求逆的一种快速算法。在强主元条件下,该法可应用于非对称阵和非正定阵。本文将该法与块预处理共轭梯度法相结合,应用于椭圆型方程数值解及类似问题的计算。数值结果表明,该法不仅适用范围较广,也具有较高的计算效率。