• Title/Summary/Keyword: Generalized Difference Methods

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GENERALIZED DIFFERENCE METHODS FOR ONE-DIMENSIONAL VISCOELASTIC PROBLEMS

  • Li, Huanrong
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.9 no.2
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    • pp.55-64
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    • 2005
  • In this paper, generalized difference methods(GDM) for one-dimensional viscoelastic problems are proposed and analyzed. The new initial values are given in the generalized difference scheme, so we obtain optimal error estimates in $L^p$ and $W^{1,p}(2\;{\leq}\;p\;{\leq}\;{\infty})$ as well as some superconvergence estimates in $W^{1,p}(2\;{\leq}\;p\;{\leq}\;{\infty})$ between the GDM solution and the generalized Ritz-Volterra projection of the exact solution.

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SECOND ORDER GENERALIZED DIFFERENCE METHODS OR ONE DIMENSIONAL PARABOLIC EQUATIONS

  • Jiang, Ziwen;Sun, Jian
    • Journal of applied mathematics & informatics
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    • v.6 no.1
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    • pp.15-30
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    • 1999
  • In this paper the second order semi-discrete and full dis-crete generalized difference schemes for one dimensional parabolic equa-tions are constructed and the optimal order $H^1$ , $L^2$ error estimates and superconvergence results in TEX>$H^1$ are obtained. The results in this paper perfect the theory of generalized difference methods.

CONVERGENCE OF FINITE DIFFERENCE METHOD FOR THE GENERALIZED SOLUTIONS OF SOBOLEV EQUATIONS

  • Chung, S.K.;Pani, A.K.;Park, M.G.
    • Journal of the Korean Mathematical Society
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    • v.34 no.3
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    • pp.515-531
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    • 1997
  • In this paper, finite difference method is applied to approximate the generalized solutions of Sobolev equations. Using the Steklov mollifier and Bramble-Hilbert Lemma, a priori error estimates in discrete $L^2$ as well as in discrete $H^1$ norms are derived frist for the semidiscrete methods. For the fully discrete schemes, both backward Euler and Crank-Nicolson methods are discussed and related error analyses are also presented.

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Equivalence of GLS and Difference Estimator in the Linear Regression Model under Seasonally Autocorrelated Disturbances

  • Seuck Heun Song;Jong Hyup Lee
    • Communications for Statistical Applications and Methods
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    • v.1 no.1
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    • pp.112-118
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    • 1994
  • The generalized least squares estimator in the linear regression model is equivalent to difference estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a seasonally autoregressive provess and autocorrelation is closed to unity.

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Stochastic analysis of elastic wave and second sound propagation in media with Gaussian uncertainty in mechanical properties using a stochastic hybrid mesh-free method

  • Hosseini, Seyed Mahmoud;Shahabian, Farzad
    • Structural Engineering and Mechanics
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    • v.49 no.1
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    • pp.41-64
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    • 2014
  • The main objective of this article is the exploitation of a stochastic hybrid mesh-free method based on stochastic generalized finite difference (SGFD), Newmark finite difference (NFD) methods and Monte Carlo simulation for thermoelastic wave propagation and coupled thermoelasticity analysis based on GN theory (without energy dissipation). A thick hollow cylinder with Gaussian uncertainty in mechanical properties is considered as an analyzed domain for the problem. The effects of uncertainty in mechanical properties with various coefficients of variations on thermo-elastic wave propagation are studied in details. Also, the time histories and distribution on thickness of cylinder of maximum, mean and variance values of temperature and radial displacement are studied for various coefficients of variations (COVs).

Eulerian-Lagrangian Split-Operator Method for the Longitudinal Dispersion Equation (종확산 방정식에 대한 Eulerian-Lagrangian 연산자 분리방법)

  • Jun, Kyung Soo;Lee, Kil Seong
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.14 no.1
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    • pp.131-141
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    • 1994
  • Three characteristics-based split-operator methods were applied to a longitudinal pollutant dispersion problem, and the results were compared with those of several Eulerian schemes. The split-operator methods consisted of generalized upwind, two-point fourth-order and sixth-order Holly-Preissmann schemes, respectively, for the advection calculation, and the Crank-Nicholson scheme for the diffusion calculation. Compared with the Eulerian schemes tested, split-operator methods using the Holly-Preissmann schemes gave much more accurate computational results. Eulerian schemes using centered difference approximations for the advection term resulted in numerical oscillations, and those using backward difference resulted in numerical diffusion, both of which were more severe for smaller value of the longitudinal dispersion coefficient.

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A Comparison of the Interval Estimations for the Difference in Paired Areas under the ROC Curves (대응표본에서 AUC차이에 대한 신뢰구간 추정에 관한 고찰)

  • Kim, Hee-Young
    • Communications for Statistical Applications and Methods
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    • v.17 no.2
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    • pp.275-292
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    • 2010
  • Receiver operating characteristic(ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of diagnostic tests is the area under the ROC curve(AUC). When two ROC curves are constructed based on two tests performed on the same individuals, statistical analysis on differences between AUCs must take into account the correlated nature of the data. This article focuses on confidence interval estimation of the difference between paired AUCs. We compare nonparametric, maximum likelihood, bootstrap and generalized pivotal quantity methods, and conduct a monte carlo simulation to investigate the probability coverage and expected length of the four methods.

AN ASSESSMENT OF PARALLEL PRECONDITIONERS FOR THE INTERIOR SPARSE GENERALIZED EIGENVALUE PROBLEMS BY CG-TYPE METHODS ON AN IBM REGATTA MACHINE

  • Ma, Sang-Back;Jang, Ho-Jong
    • Journal of applied mathematics & informatics
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    • v.25 no.1_2
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    • pp.435-443
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    • 2007
  • Computing the interior spectrum of large sparse generalized eigenvalue problems $Ax\;=\;{\lambda}Bx$, where A and b are large sparse and SPD(Symmetric Positive Definite), is often required in areas such as structural mechanics and quantum chemistry, to name a few. Recently, CG-type methods have been found useful and hence, very amenable to parallel computation for very large problems. Also, as in the case of linear systems proper choice of preconditioning is known to accelerate the rate of convergence. After the smallest eigenpair is found we use the orthogonal deflation technique to find the next m-1 eigenvalues, which is also suitable for parallelization. This offers advantages over Jacobi-Davidson methods with partial shifts, which requires re-computation of preconditioner matrx with new shifts. We consider as preconditioners Incomplete LU(ILU)(0) in two variants, ever-relaxation(SOR), and Point-symmetric SOR(SSOR). We set m to be 5. We conducted our experiments on matrices from discretizations of partial differential equations by finite difference method. The generated matrices has dimensions up to 4 million and total number of processors are 32. MPI(Message Passing Interface) library was used for interprocessor communications. Our results show that in general the Multi-Color ILU(0) gives the best performance.

Lp and W1,p Error Estimates for First Order GDM on One-Dimensional Elliptic and Parabolic Problems

  • Gong, Jing;Li, Qian
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.4 no.2
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    • pp.41-57
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    • 2000
  • In this paper, we consider first order generalized difference scheme for the two-point boundary value problem and one-dimensional second order parabolic type problem. The optimal error estimates in $L_p$ and $W^{1,p}$ ($2{\leq}p{\leq}{\infty}$) as well as some superconvergence estimates in $W^{1,p}$ ($2{\leq}p{\leq}{\infty}$) are obtained. The main results in this paper perfect the theory of GDM.

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