多孔介质中可压缩可混溶驱动问题的共轭梯度迭代法的L2模误差估计

2006年3月 高等学校计算数学学报 1数学模型 多孔介质中可压缩可混溶驱动问题的模型是两个非线性抛物型方程:压力方程和饱 和度方程.Douglass和Roberts曾提出其数学模型并研究了半离散化方法[“一”}.袁益让对 此模型研究了特征一有限元方法[s]和差分法10]. 本人对可压缩可混溶驱动问题的模型曾研究了共扼梯度迭代解与原问题真解的最优 阶H‘模误差估计阁.其中饱和度方程的弥散项为一甲·(D(劝甲c)，而本文讨论的是D(司 情况下的尸模误差估计.就护模而言，对此模型目前尚未有人讨论过.从本文可看到， 由于饱和度方程中含有拭c)鬓这一项，…     

Application of a block modified Chebyshev algorithm to the iterative solution of symmetric linear systems with multiple right hand side vectors

Summary. An adaptive Richardson iteration method is described for the solution of large sparse symmetric positive definite linear systems of equations with multiple right-hand side vectors. This scheme ``learns' about the linear system to be solved by computing inner products of residual matrices during the iterations. These inner products are interpreted as block modified moments. A block version of the modified Chebyshev algorithm is presented which yields a block tridiagonal matrix from the block modified moments and the recursion coefficients of the residual polynomials. The eigenvalues of this block tridiagonal matrix define an interval, which determines the choice of relaxation parameters for Richardson iteration. Only minor modifications are necessary in order to obtain a scheme for the solution of symmetric indefinite linear systems with multiple right-hand side vectors. We outline the changes required. Received April 22, 1993     

Another Conjugate Gradient Algorithm with Guaranteed Descent and Conjugacy Conditions for Large-scale Unconstrained Optimization

In this paper, we suggest another accelerated conjugate gradient algorithm for which both the descent and the conjugacy conditions are guaranteed. The search direction is selected as a linear combination of the gradient and the previous direction. The coefficients in this linear combination are selected in such a way that both the descent and the conjugacy condition are satisfied at every iteration. The algorithm introduces the modified Wolfe line search, in which the parameter in the second Wolfe condition is modified at every iteration. It is shown that both for uniformly convex functions and for general nonlinear functions, the algorithm with strong Wolfe line search generates directions bounded away from infinity. The algorithm uses an acceleration scheme modifying the step length in such a manner as to improve the reduction of the function values along the iterations. Numerical comparisons with some conjugate gradient algorithms using a set of 75 unconstrained optimization problems with different dimensions show that the computational scheme outperforms the known conjugate gradient algorithms like Hestenes and Stiefel; Polak, Ribière and Polyak; Dai and Yuan or the hybrid Dai and Yuan; CG_DESCENT with Wolfe line search, as well as the quasi-Newton L-BFGS.     

A Class of Nested Iteration Schemes for Linear Systems with a Coefficient Matrix with a Dominant Positive Definite Symmetric Part

We present a class of nested iteration schemes for solving large sparse systems of linear equations with a coefficient matrix with a dominant symmetric positive definite part. These new schemes are actually inner/outer iterations, which employ the classical conjugate gradient method as inner iteration to approximate each outer iterate, while each outer iteration is induced by a convergent and symmetric positive definite splitting of the coefficient matrix. Convergence properties of the new schemes are studied in depth, possible choices of the inner iteration steps are discussed in detail, and numerical examples from the finite-difference discretization of a second-order partial differential equation are used to further examine the effectiveness and robustness of the new schemes over GMRES and its preconditioned variant. Also, we show that the new schemes are, at least, comparable to the variable-step generalized conjugate gradient method and its preconditioned variant.     

A hybrid iterative method for symmetric indefinite linear systems

Hybrid iterative methods that combine a conjugate direction method with a simpler iteration scheme, such as Chebyshev or Richardson iteration, were first proposed in the 1950s. The ease with which Chebyshev and Richardson iteration can be implemented efficiently on a large variety of computer architectures has in recent years lead to renewed interest in iterative methods that use Chebyshev or Richardson iteration. This paper presents a new hybrid iterative method for the solution of linear systems of equations with a symmetric indefinite matrix. Our method combines the conjugate residual method with Richardson iteration. Special attention is paid to the determination of two real intervals, one on each side of the origin, that contain most of the eigenvalues of the matrix. These intervals are used to compute suitable iteration parameters for Richardson iteration. We also discuss when to switch between the methods. The hybrid scheme typically uses the Richardson method for most iterations, and this reduces the number of arithmetic vector operations significantly compared with the number of arithmetic vector operations required when only the conjugate residual method is used. Computed examples illustrate the competitiveness of the hybrid scheme.     

On two numerical schemes of the Monte Carlo method for solving the Boltzmann equation

Two numerical schemes of the Monte Carlo method for solving the Cauchy problem for the Boltzmann equation are constructed and tested. They are based on a well-known relationship between a nonlinear integral equation and a random process. Procedures for modeling special random processes on whose trajectories unbiased estimators for the solution are described. Each scheme has its own domain of applicability, in which its advantages manifest themselves. The conjugate scheme is convenient for calculating the Boltzmann distribution function at high velocities (on “tails”). For the example of the BKW solution, the applicability of the schemes is numerically analyzed.     

Dirichlet Problem for a Class of Quasilinear Elliptic Equations

In this paper, the Dirichlet problem for quasilinear elliptic equations is studied. New a priori estimates of the solution and its gradient are obtained. These estimates are derived without any assumptions on the smoothness of the coefficients and the right-hand side of the equation. Moreover, an arbitrary growth of the right-hand side with respect to the gradient of the solution is assumed. On the basis of the resulting estimates, existence theorems are proved.     

A Finite Volume Method Preserving Maximum Principle for the Conjugate Heat Transfer Problems with General Interface Conditions

In this paper, we present a unified finite volume method preserving discrete maximum principle (DMP) for the conjugate heat transfer problems with general interface conditions. We prove the existence of the numerical solution and the DMP-preserving property. Numerical experiments show that the nonlinear iteration numbers of the scheme in [24] increase rapidly when the interfacial coefficients decrease to zero. In contrast, the nonlinear iteration numbers of the unified scheme do not increase when the interfacial coefficients decrease to zero, which reveals that the unified scheme is more robust than the scheme in [24]. The accuracy and DMP-preserving property of the scheme are also veried in the numerical experiments.     

Monotone schemes for a class of nonlinear elliptic and parabolic problems

We construct monotone numerical schemes for a class of nonlinear PDE for elliptic and initial value problems for parabolic problems. The elliptic part is closely connected to a linear elliptic operator, which we discretize by monotone schemes, and solve the nonlinear problem by iteration. We assume that the elliptic differential operator is in the divergence form, with measurable coefficients satisfying the strict ellipticity condition, and that the right-hand side is a positive Radon measure. The numerical schemes are not derived from finite difference operators approximating differential operators, but rather from a general principle which ensures the convergence of approximate solutions. The main feature of these schemes is that they possess stencils stretching far from basic grid-rectangles, thus leading to system matrices which are related to M-matrices.     

Regularizing properties of a truncated newton-cg algorithm for nonlinear inverse problems

This paper develops truncated Newton methods as an appropriate tool for nonlinear inverse problems which are ill-posed in the sense of Hadamard. In each Newton step an approximate solution for the linearized problem is computed with the conjugate gradient method as an inner iteration. The conjugate gradient iteration is terminated when the residual has been reduced to a prescribed percentage. Under certain assumptions on the nonlinear operator it is shown that the algorithm converges and is stable if the discrepancy principle is used to terminate the outer iteration. These assumptions are fulfilled, e.g., for the inverse problem of identifying the diffusion coefficient in a parabolic differential equation from distributed data.     

Cascadic multilevel methods for ill-posed problems

Multilevel methods are popular for the solution of well-posed problems, such as certain boundary value problems for partial differential equations and Fredholm integral equations of the second kind. However, little is known about the behavior of multilevel methods when applied to the solution of linear ill-posed problems, such as Fredholm integral equations of the first kind, with a right-hand side that is contaminated by error. This paper shows that cascadic multilevel methods with a conjugate gradient-type method as basic iterative scheme are regularization methods. The iterations are terminated by a stopping rule based on the discrepancy principle.     

Convergence order of implicit Euler numerical scheme for maximal monotone differential inclusions

In the present work, we deal with the convergence of a class of numerical schemes for maximal monotone evolution systems in the particular case where the maximal monotone term is a subdifferential of a convex proper and lower semi-continuous function and the right-hand side depends on time and on solution. More precisely, we focus on an implicit Euler scheme and we show that the order of this scheme is one. Finally, some applications are given for a large class of rheological models.     

An axisymmetric problem of an embedded mixed-mode crack in a functionally graded magnetoelectroelastic infinite medium

This paper investigates the problem of an axisymmetric penny shaped crack embedded in an infinite functionally graded magneto electro elastic medium. The loading consists of magnetoelectromechanical loads applied on the crack surfaces assumed to be magneto electrically impermeable. The material’s gradient is parallel to the axisymmetric direction and is perpendicular to the crack plane. An anisotropic constitutive law is adopted to model the material behavior. The governing equations are converted analytically using Hankel transform into coupled singular integral equations, which are solved numerically to yield the crack tip stress, electric displacement and magnetic induction intensity factors. A similar problem but with a different crack morphology, that is a plane crack embedded in an infinite functionally graded magneto electro elastic medium, was considered by the authors in a previous work (Rekik et al., 2012) [25]. While the overall solution schemes look similar, the axisymmetric problem resulted in more mathematical complexities and let to different conclusions with respect to the influence of coupling between elastic, electric and magnetic effects. The main focus of this paper is to study the effect of material non-homogeneity on the fields’ intensity factors to understand further the behavior of graded magnetoelectroelastic materials containing penny shaped cracks and to inspect the effect of varying the crack geometry.     

A descent spectral conjugate gradient method for impulse noise removal

In most applications, denoising image is fundamental to subsequent image processing operations. This paper proposes a spectral conjugate gradient (CG) method for impulse noise removal, which is based on a two-phase scheme. The noise candidates are first identified by the adaptive (center-weighted) median filter; then these noise candidates are restored by minimizing an edge-preserving regularization functional, which is accomplished by the proposed spectral CG method. A favorite property of the proposed method is that the search direction generated at each iteration is descent. Under strong Wolfe line search conditions, its global convergence result could be established. Numerical experiments are given to illustrate the efficiency of the spectral conjugate gradient method for impulse noise removal.     

Reuse,Recycle, Reduce (3R) – strategies for the calculation of transient magnetic fields

The discretization of transient magneto-dynamic field problems with geometric discretization schemes such as the Finite Integration Technique or the Finite-Element Method based on Whitney form functions results in nonlinear differential-algebraic systems of equations of index 1. Their time integration with embedded s-stage singly diagonal implicit Runge–Kutta methods requires the solution of s nonlinear systems within one time step. Accelerated solution of these schemes is achieved with techniques following so-called 3R-strategies (“reuse, recycle, reduce”). This involves e.g. the solution of the linear(-ized) equations in each time step where the solution process of the iterative preconditioned conjugate gradient method reuses and recycles spectral information of linear systems from previous stages. Additionally, a combination of an error controlled spatial adaptivity and an error controlled implicit Runge–Kutta scheme is used to reduce the number of unknowns for the algebraic problems effectively and to avoid unnecessary fine grid resolutions both in space and time. First numerical results for 2D nonlinear magneto-dynamic problems validate the presented approach and its implementation. The space discretization in the numerical examples is done by Lagrangian nodal finite elements but the presented algorithms also work in combination with other discretization schemes for the Maxwell equations such as the Whitney vector finite elements.     

Accuracy,robustness and efficiency comparison in iterative computation of convection diffusion equation with boundary layers

Nine‐point fourth‐order compact finite difference scheme, central difference scheme, and upwind difference scheme are compared for solving the two‐dimensional convection diffusion equations with boundary layers. The domain is discretized with a stretched nonuniform grid. A grid transformation technique maps the nonuniform grid to a uniform one, on which the difference schemes are applied. A multigrid method and a multilevel preconditioning technique are used to solve the resulting sparse linear systems. We compare the accuracy of the computed solutions from different discretization schemes, and demonstrate the relative efficiency of each scheme. Comparisons of maximum absolute errors, iteration counts, CPU timings, and memory cost are made with respect to the two solution strategies. © 2000 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 16: 379–394, 2000     

Reconstruction of biologic tissue conductivity from noisy magnetic field by integral equation method

Magnetic resonance electrical impedance tomography (MREIT) is a new technique to recover the conductivity of biologic tissue from the induced magnetic flux density. This paper proposes an inversion scheme for recovering the conductivity from one component of the magnetic field based on the nonlinear integral equation method. To apply magnetic fields corresponding to two incoherent injected currents, an alternative iteration scheme is proposed to update the conductivity. For each magnetic field, the regularizing technique on the finite dimensional space is applied to solve an ill-posed linear system. Compared with the well-developed harmonic B_z method, the advantage of this inversion scheme is its stability, since no differential operation is required on the noisy magnetic field. Numerical implementations are given to show the convergence of the iteration and its validity for noisy input data.     

A tearing-based hybrid parallel banded linear system solver

A new parallel algorithm for the solution of banded linear systems is proposed. The scheme tears the coefficient matrix into several overlapped independent blocks in which the size of the overlap is equal to the system’s bandwidth. A corresponding splitting of the right-hand side is also provided. The resulting independent, and smaller size, linear systems are solved under the constraint that the solutions corresponding to the overlap regions are identical. This results in a linear system whose size is proportional to the sum of the overlap regions which we refer to as the “balance” system. We propose a solution strategy that does not require obtaining this “balance” system explicitly. Once the balance system is solved, retrieving the rest of the solution can be realized with almost perfect parallelism. Our proposed algorithm is a hybrid scheme that combines direct and iterative methods for solving a single banded system of linear equations on parallel architectures. It has broad applications in finite-element analysis, particularly as a parallel solver of banded preconditioners that can be used in conjunction with outer Krylov iterative schemes.     

Variational Principle for General Diffusion Problems

We employ the Monge–Kantorovich mass transfer theory to study the existence of solutions for a large class of parabolic partial differential equations. We deal with nonhomogeneous nonlinear diffusion problems (of Fokker–Planck type) with time-dependent coefficients. This work greatly extends the applicability of known techniques based on constructing weak solutions by approximation with time-interpolants of minimizers arising from Wasserstein-type implicit schemes. It also generalizes previous results of the authors, where proofs of convergence in the case of a right-hand side in the equation is given by these methods. To prove the existence of weak solutions we establish an interesting maximum principle for such equations. This involves comparison with the solution for the corresponding homogeneous, time-independent equation.     

A domain decomposition method using efficient interface-acting preconditioners

The conjugate gradient boundary iteration (CGBI) is a domain decomposition method for symmetric elliptic problems on domains with large aspect ratio. High efficiency is reached by the construction of preconditioners that are acting only on the subdomain interfaces. The theoretical derivation of the method and some numerical results revealing a convergence rate of 0.04-0.1 per iteration step are given in this article. For the solution of the local subdomain problems, both finite element (FE) and spectral Chebyshev methods are considered.