SEBSM-Based Iterative Method for Solving Large Systems of Linear Equations and Its Applications in Engineering Computation

2016 ◽  
Vol 13 (05) ◽  
pp. 1650024 ◽  
Author(s):  
Jin-Xiu Hu ◽  
Xiao-Wei Gao ◽  
Zhi-Chao Yuan ◽  
Jian Liu ◽  
Shi-Zhang Huang
Keyword(s):  
Iterative Method ◽  
Direct Method ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Relaxation Factor ◽  
Banded Matrix ◽  
Speed Up ◽  
Systems Of Linear Equations ◽  
Matrix Formula ◽  
Large Systems

In this paper, a new iterative method, for solving sparse nonsymmetrical systems of linear equations is proposed based on the Simultaneous Elimination and Back-Substitution Method (SEBSM), and the method is applied to solve systems resulted in engineering problems solved using Finite Element Method (FEM). First, SEBSM is introduced for solving general linear systems using the direct method. And, then an iterative method based on SEBSM is presented. In the method, the coefficient matrix [Formula: see text] is split into lower, diagonally banded and upper matrices. The iterative convergence can be controlled by selecting a suitable bandwidth of the diagonally banded matrix. And the size of the working array needing to be stored in iteration is as small as the bandwidth of the diagonally banded matrix. Finally, an accelerating strategy for this iterative method is proposed by introducing a relaxation factor, which can speed up the convergence effectively if an optimal relaxation factor is chosen. Two numerical examples are given to demonstrate the behavior of the proposed method.

ACCUMULATION OF THE CHOLESKY SQUARE ROOT IN HELMERT BLOCKING

1986 ◽  
Vol 40 (3) ◽  
pp. 297-314
Author(s):  
D. R. Junkins ◽  
R. R. Steeves
Keyword(s):  
Efficient Solution ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Computational Method ◽  
Normal Equation ◽  
Square Root ◽  
Unknown Parameters ◽  
Normal Equations ◽  
Systems Of Linear Equations ◽  
Large Systems

The Helmert blocking method is being used in the present effort to readjust North American geodetic networks. Combining this method with the Cholesky computational method enables the efficient solution of very large systems of linear equations. A by-product of this approach is a “partial” Cholesky square root for each Helmert block. This paper demonstrates that the Cholesky square root for the entire system of normal equations can be constructed from partial Cholesky square root blocks that are produced during the Helmert block adjustment, even though various reorderings of the unknown parameters are necessary throughout the computations. The entire Cholesky square root can be used to compute the inverse of the normal equation coefficient matrix, which is needed for post-adjustment statistical analyses.

scholarly journals A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Zhijun Luo ◽  
Lirong Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Descent Direction ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Globally Convergent ◽  
Variable Distribution ◽  
Systems Of Linear Equations

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

3-1 Piecewise NCP Function for New Nonmonotone QP-Free Infeasible Method

2014 ◽  
Vol 26 (5) ◽  
pp. 566-572 ◽  
Author(s):  
Ailan Liu ◽  
◽  
Dingguo Pu ◽  
◽  
Keyword(s):  
Constraint Qualification ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Ncp Function ◽  
Line Searches ◽  
Complementarity Condition ◽  
Linear Independence Constraint Qualification ◽  
Systems Of Linear Equations

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/04.jpg"" width=""300"" />Algorithm flow chart</div> We propose a nonmonotone QP-free infeasible method for inequality-constrained nonlinear optimization problems based on a 3-1 piecewise linear NCP function. This nonmonotone QP-free infeasible method is iterative and is based on nonsmooth reformulation of KKT first-order optimality conditions. It does not use a penalty function or a filter in nonmonotone line searches. This algorithm solves only two systems of linear equations with the same nonsingular coefficient matrix, and is implementable and globally convergent without a linear independence constraint qualification or a strict complementarity condition. Preliminary numerical results are presented. </span>

scholarly journals On the stability of ADI methods

2017 ◽  
Vol 42 (598) ◽  
Author(s):  
Ole Østerby
Keyword(s):  
Linear Equations ◽  
Implicit Methods ◽  
Stability Properties ◽  
Require Time ◽  
Jump Discontinuities ◽  
Systems Of Linear Equations ◽  
Two Space Dimensions ◽  
Large Systems ◽  
Adi Methods ◽  
The Stability

When solving parabolic equations in two space dimensions implicit methods are preferred to the explicit method because of their better stability properties. Straightforward implementation of implicit methods require time-consuming solution of large systems of linear equations, and ADI methods are preferred instead. We expect the ADI methods to inherit the stability properties of the implicit methods they are derived from, and we demonstrate that this is partly true. The Douglas-Rachford and Peaceman-Rachford methods are absolutely stable in the sense that their growth factors are ≤ 1 in absolute value. Near jump discontinuities, however, there are differences w.r.t. how the ADI methods react to the situation: do they produce oscillations and how effectively do they damp them. We demonstrate the behaviour on two simple examples.

scholarly journals Numerical Simulation of Multiphase Multicomponent Flow in Porous Media: Efficiency Analysis of Newton-Based Method

Fluids ◽  
2021 ◽  
Vol 6 (10) ◽  
pp. 355
Author(s):  
Timur Imankulov ◽  
Danil Lebedev ◽  
Bazargul Matkerim ◽  
Beimbet Daribayev ◽  
Nurislam Kassymbek
Keyword(s):  
Porous Media ◽  
Linear Equations ◽  
Computation Time ◽  
Flow In Porous Media ◽  
Multicomponent Flow ◽  
Original Matrix ◽  
Sparsity Pattern ◽  
Speed Up ◽  
Systems Of Linear Equations ◽  
The One

Newton’s method has been widely used in simulation multiphase, multicomponent flow in porous media. In addition, to solve systems of linear equations in such problems, the generalized minimal residual method (GMRES) is often used. This paper analyzed the one-dimensional problem of multicomponent fluid flow in a porous medium and solved the system of the algebraic equation with the Newton-GMRES method. We calculated the linear equations with the GMRES, the GMRES with restarts after every m steps—GMRES (m) and preconditioned with Incomplete Lower-Upper factorization, where the factors L and U have the same sparsity pattern as the original matrix—the ILU(0)-GMRES algorithms, respectively, and compared the computation time and convergence. In the course of the research, the influence of the preconditioner and restarts of the GMRES (m) algorithm on the computation time was revealed; in particular, they were able to speed up the program.

Sign in / Sign up

Export Citation Format

Share Document