2 edition of **Preconditioned conjugate gradient methods on the DAP** found in the catalog.

Preconditioned conjugate gradient methods on the DAP

C. H. Lai

- 11 Want to read
- 17 Currently reading

Published
**1987**
by Queen Mary College, Department of Computer Science and Statistics in London
.

Written in English

**Edition Notes**

Statement | C.H. Lai and H.M. Liddell. |

Series | Report -- No. 413 |

Contributions | Liddell, H. M., Queen Mary College. Department of Computer Science and Statistics. |

The Physical Object | |
---|---|

Pagination | 8, [4]p. |

ID Numbers | |

Open Library | OL13934548M |

() Moving force identification based on modified preconditioned conjugate gradient method. Journal of Sound and Vibration , () Improvement of Block IC Preconditioner Using Fill-In Technique for Linear Systems Derived From Finite-Element Method Including Thin . The authors consider the solution of least squares problems min $\| b - Tx \|_2 $ by the preconditioned conjugate gradient method, for m-by-n complex Toeplitz matrices T of rank n.A circulant preconditioner C is derived using the T. Chars optimal preconditioner on n-by-n Toeplitz row blocks of Toeplitz T that are generated by $2\pi $-periodic continuous complex-valued functions .

A robust numerical method called the Preconditioned Bi-Conjugate Gradient (Pre-BiCG)method is proposed for the solution of radiative transfer equation in spherical geometry.A variant of this method called Stabilized Preconditioned Bi-Conjugate Gradient (Pre-BiCG-STAB) is also presented. Book. Jan ; J. Rawlings numerical experiments are presented in order to show the reduction of computational cost and number of iteration of the preconditioned conjugate gradient method.

IML++ (Iterative Methods Library) v. a IML++ is a C++ templated library of modern iterative methods for solving both symmetric and nonsymmetric linear systems of equations. The algorithms are fully templated in that the same source code works for . Preconditioned conjugate gradient methods for adaptive filtering. , IEEE International Sympoisum on Circuits and Systems, A preconditioning technique for fast iterative image restoration.

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The Nonlinear Conjugate Gradient Method 42 Outline of the Nonlinear Conjugate Gradient Method 42 General Line Search 43 Preconditioning 47 A Notes 48 B Canned Algorithms 49 B1. Steepest Descent 49 B2. Conjugate Gradients 50 B3. Preconditioned Conjugate Gradients 51 i.

Preconditioned Conjugate Gradient Methods Proceedings of a Conference held in Nijmegen, The Netherlands, June 19–21, Buy Preconditioned Conjugate Gradient Methods: Proceedings of a Conference held in Nijmegen, The Netherlands, June(Lecture Notes in Mathematics) on FREE SHIPPING on.

This method is referred to as incomplete Cholesky factorization (see the book by Golub and van Loan for more details).

Remark The Matlab script PCGDemo.m illustrates the convergence behavior of the preconditioned conjugate gradient algorithm. The matrix A here is a × sym-metric positive deﬁnite matrix with all zeros except a ii = File Size: KB.

Three versions of conjugate gradient method — the bi-conjugate gradient method (bi-CG), conjugate gradients squared (CGS) and its variant bi-CGSTAB - are compared with the Gauss elimination direct method.

Different types of preconditioning of matrices are tested including Jacobi and incomplete factorisation (ILU) by: 9.

The preconditioned conjugate-gradient method (Coneus, Golub and O'Leary, ) is an iterative method which can be used to solve matrix equations if the matrix is symmetric (matrix element aij = aji, where the first subscript is the matrix-row number, and the second is the matrix.

The motivation for this conference was the wish to bring together specialists working on iterative solution methods, in particular using preconditioning methods.

The topics presented at the conference contained both original analysis and implementational aspects of preconditioned conjugate gradient methods. In this paper the preconditioned conjugate gradient method is used to solve the system of linear equations Ax = b, where A is a singular symmetric positive semi-definite matrix.

The method diverges if b is not exactly in the range R(A) of the null space N(A) of A is explicitly known, then this divergence can be avoided by subtracting from b its orthogonal projection onto N(A).

Weicheng Huang, Danesh Tafti, in Parallel Computational Fluid Dynamics3 ADDITIVE SCHWARZ PRECONDITIONED CG METHOD. Conjugate gradient (CG) methods are used widely for solving large sparse linear systems Au = f.

Usually, an equivalent preconditioned system M −1 Au = M −1 f which exhibits better spectral properties is solved. M is the preditioning matrix or the. Ref. discusses the conjugate gradient (CG) method and the preconditioned CG method for solving linear equations for symmetric systems.

Proposition In algorithms for nonsymmetric systems, there are the relations for the right-preconditioned system and for the left-preconditioned system, in which k is the number of iterations. viii CONTENTS Convergence of GMRES Block Krylov Methods A class of preconditioned conjugate gradient methods applied to finite element equations.

Pages Gustafsson, Ivar. Preview. Book Title Preconditioned Conjugate Gradient Methods Book Subtitle Proceedings of a Conference held in Nijmegen, The Netherlands, June 19. the locally optimal steepest descent method. In both the original and the preconditioned conjugate gradient methods one only needs to set:= in order to make them locally optimal, using the line search, steepest descent methods.

With this substitution, vectors p are always the same as vectors z, so there is no need to store vectorsevery iteration of these steepest descent methods. Abstract. The conjugate gradient method was published by Hestenes and Stiefel inas a direct method for solving linear systems.

Today its main use is as an iterative method for solving large sparse linear systems. On a test problem we show that it performs as well as the SOR method with optimal acceleration parameter, and we do not have to estimate any such parameter.

Abstract. In this study, for solving the three-dimensional partial differential equation u t = u xx + u yy + u zz, an efficient parallel method based on the modified incomplete Cholesky preconditioned conjugate gradient algorithm (MICPCGA) on the GPU is our proposed method, for this case, we overcome the drawbacks that the MIC preconditioner is generally difficult to.

Preconditioning for linear systems. In linear algebra and numerical analysis, a preconditioner of a matrix is a matrix such that − has a smaller condition number is also common to call = − the preconditioner, rather than, since itself is rarely explicitly available.

In modern preconditioning, the application of = −, i.e., multiplication of a column vector, or a block of column. AXELSSON and I. GUSTAFSSON, On the use of Preconditioned Conjugate Gradient Methods for Red-Black Ordered Five-point Difference Schemes, J. Comp. Physics 35 (), – MathSciNet CrossRef zbMATH Google Scholar.

() On the efficient implementation of preconditioned s-step conjugate gradient methods on multiprocessors with memory hierarchy. Parallel Computing() Accelerated simultaneous iterations for large finite element eigenproblems. A Multigrid Preconditioned Conjugate Gradient Method for Large Scale Wavefront Reconstruction (1), (1) and roek (2) (1) Montana State University, Dept.

of Mathematical Sciences, Bozeman, Montana (2) Gemini Observatory, N. SOLUTION BY THE PRECONDITIONED CONJUGATE-GRADIENT METHOD The preconditioned conjugate-gradient method for solving a set of linear equations is iterative. In iterative methods, it is assumed that the matrix A_ can be split into the sum of two matrices; that is A_ = M_ + N (Varga,p.

; Remson and others,p. Instead of solving the least squares problems directly, we transform them into a batch of saddle point linear systems and subsequently solve the linear systems using restrictively preconditioned conjugate gradient (RPCG) methods.

Approximation of the new Schur complement is generated effectively based on a previously approximated Schur complement.Large part of the book is devoted to preconditioned conjugate gradient algorithms.

In particular memoryless and limited memory quasi-Newton algorithms are presented and numerically compared to standard conjugate gradient algorithms. The special attention is paid to the methods of shortest residuals developed by the author.

Application of the SSOR preconditioned CG algorithm to the vector FEM for 3D full-wave analysis of electromagnetic-field boundary-value problems Abstract: The symmetric successive overrelaxation (SSOR) preconditioning scheme is applied to the conjugate-gradient (CG) method for solving a large system of linear equations resulting from the use of.