• Title/Summary/Keyword: linear convergence

Search Result 1,329, Processing Time 0.027 seconds

A MIXED-TYPE SPLITTING ITERATIVE METHOD

  • Jiang, Li;Wang, Ting
    • Journal of applied mathematics & informatics
    • /
    • v.29 no.5_6
    • /
    • pp.1067-1074
    • /
    • 2011
  • In this paper, a preconditioned mixed-type splitting iterative method for solving the linear systems Ax = b is presented, where A is a Z-matrix. Then we also obtain some results to show that the rate of convergence of our method is faster than that of the preconditioned AOR (PAOR) iterative method and preconditioned SOR (PSOR) iterative method. Finally, we give one numerical example to illustrate our results.

MULTIGRID METHODS FOR THE PURE TRACTION PROBLEM OF LINEAR ELASTICITY: FOSLS FORMULATION

  • Lee, Chang-Ock
    • Communications of the Korean Mathematical Society
    • /
    • v.12 no.3
    • /
    • pp.813-827
    • /
    • 1997
  • Multigrid methods for two first-order system least squares (FOSLS) using bilinear finite elements are developed for the pure traction problem of planar linear elasticity. They are two-stage algorithms that first solve for the gradients of displacement, then for the displacement itself. In this paper, concentration is given on solving for the gradients of displacement only. Numerical results show that the convergences are uniform even as the material becomes nearly incompressible. Computations for convergence rates are included.

  • PDF

ENUMERATION OF OPTIMALLY LABELLED GRAPHS OF BANDWIDTH 2

  • Chae, Gab-Byung;Cheong, MinSeok;Kim, Sang-Mok
    • Bulletin of the Korean Mathematical Society
    • /
    • v.54 no.6
    • /
    • pp.1883-1891
    • /
    • 2017
  • An optimally labelled graph of bandwidth 2 is an ordered pair (G, f) where G is a simple graph with bw(G) = 2 and $f:V(G){\rightarrow}[n]$ is a bijection such that bw(G, f) = 2. In this paper, the number of optimally labelled graphs of bandwidth two of order n is enumerated by counting linear forests.

Comparison with two Gradient Methods through the application to the Vector Linear Predictor (두가지 gradient 방법의 벡터 선형 예측기에 대한 적용 비교)

  • Shin, Kwang-Kyun;Yang, Seung-In
    • Proceedings of the KIEE Conference
    • /
    • 1987.07b
    • /
    • pp.1595-1597
    • /
    • 1987
  • Two gradient methods, steepest descent method and conjugate gradient descent method, are compar ed through application to vector linear predictors. It is found that the convergence rate of the conju-gate gradient descent method is much faster than that of the steepest descent method.

  • PDF

Optimal Power Flow with Linear Programming (선형계획법을 이용한 최적조류계산)

  • Jung, G.H.;Baek, Y.S.;Song, K.B.;Chu, J.B.;Won, J.R.
    • Proceedings of the KIEE Conference
    • /
    • 2000.11a
    • /
    • pp.145-148
    • /
    • 2000
  • This paper presents new algorithm which is based on LP(Linear Programming) that guarantee convergence. It is considered to minimize generation cost and load shedding as object function subject to various constraints. The proposed algorithm use sensitivity matrix to re-dispatch generation power, so the total CPU time is saved.

  • PDF

IMPROVING COMPARISON RESULTS ON PRECONDITIONED GENERALIZED ACCELERATED OVERRELAXATION METHODS

  • Wang, Guangbin;Sun, Deyu
    • Journal of applied mathematics & informatics
    • /
    • v.33 no.1_2
    • /
    • pp.193-201
    • /
    • 2015
  • In this paper, we present preconditioned generalized accelerated overrelaxation (GAOR) methods for solving weighted linear least square problems. We compare the spectral radii of the iteration matrices of the preconditioned and the original methods. The comparison results show that the preconditioned GAOR methods converge faster than the GAOR method whenever the GAOR method is convergent. Finally, we give a numerical example to confirm our theoretical results.

ESOR METHOD WITH DIAGONAL PRECONDITIONERS FOR SPD LINEAR SYSTEMS

  • Oh, Seyoung;Yun, Jae Heon;Kim, Kyoum Sun
    • Journal of applied mathematics & informatics
    • /
    • v.33 no.1_2
    • /
    • pp.111-118
    • /
    • 2015
  • In this paper, we propose an extended SOR (ESOR) method with diagonal preconditioners for solving symmetric positive definite linear systems, and then we provide convergence results of the ESOR method. Lastly, we provide numerical experiments to evaluate the performance of the ESOR method with diagonal preconditioners.

ERROR ESTIMATES OF FULLY DISCRETE DISCONTINUOUS GALERKIN APPROXIMATIONS FOR LINEAR SOBOLEV EQUATIONS

  • Ohm, M.R.;Shin, J.Y.;Lee, H.Y.
    • Journal of applied mathematics & informatics
    • /
    • v.27 no.5_6
    • /
    • pp.1221-1234
    • /
    • 2009
  • In this paper, we construct fully discrete discontinuous Galerkin approximations to the solution of linear Sobolev equations. We apply a symmetric interior penalty method which has an interior penalty term to compensate the continuity on the edges of interelements. The optimal convergence of the fully discrete discontinuous Galerkin approximations in ${\ell}^{\infty}(L^2)$ norm is proved.

  • PDF

CONDITIONAL EXPECTATION OF PETTIS INTEGRABLE UNBOUNDED RANDOM SETS

  • El Harami, Mohamed
    • Journal of the Korean Mathematical Society
    • /
    • v.57 no.2
    • /
    • pp.359-381
    • /
    • 2020
  • In this paper we established new results of existence of conditional expectation for closed convex and unbounded Pettis integrable random sets without assuming the Radon Nikodym property of the Banach space. As application, new versions of multivalued Lévy's martingale convergence theorem are proved by using the Slice and the linear topologies.

AN ABS ALGORITHM FOR SOLVING SINGULAR NONLINEAR SYSTEMS WITH RANK DEFECTS

  • Ge, Rendong;Xia, Zun-Quan
    • Journal of applied mathematics & informatics
    • /
    • v.12 no.1_2
    • /
    • pp.1-20
    • /
    • 2003
  • A modified ABS algorithm for solving a class of singular non-linear systems, $F(x) = 0, $F\;\in \;R^n$, constructed by combining the discreted ABS algorithm and a method of Hoy and Schwetlick (1990), is presented. The second differential operation of F at a point is not required to be calculated directly in this algorithm. Q-quadratic convergence of this algorithm is given.