预处理CG算法解油藏模拟问题的有效性比较

1引言 在大型科学和工程计算问题的实际应用中，经常会遇到求解除椭圆型或抛物线型偏微分方程问题。经差分法或有限元方法离散化后得到一个大型稀疏线性方程组。本文比较了几     

Runge-Kutta Discretizations of Singularly Perturbed Gradient Equations

We analyze Runge-Kutta discretizations applied to singularly perturbed gradient systems. It is shown in which sense the discrete dynamics preserve the geometric properties and the longtime behavior of the underlying ordinary differential equation. If the continuous system has an attractive invariant manifold then numerical trajectories started in some neighbourhood (the size of which is independent of the step-size and the stiffness parameter) approach an equilibrium in a nearby manifold. The proof combines invariant manifold techniques developed by Nipp and Stoffer for singularly perturbed systems with some recent results of the second author on the global behavior of discretized gradient systems. The results support the favorable behavior of ODE methods for stiff minimization problems.     

Suitable iterative methods for solving the linear system arising in the three fields domain decomposition method

There are two approaches for applying substructuring preconditioner for the linear system corresponding to the discrete Steklov–Poincaré operator arising in the three fields domain decomposition method for elliptic problems. One of them is to apply the preconditioner in a common way, i.e. using an iterative method such as preconditioned conjugate gradient method [S. Bertoluzza, Substructuring preconditioners for the three fields domain decomposition method, I.A.N.-C.N.R, 2000] and the other one is to apply iterative methods like for instance bi-conjugate gradient method, conjugate gradient square and etc. which are efficient for nonsymmetric systems (the preconditioned system will be nonsymmetric). In this paper, second approach will be followed and extensive numerical tests will be presented which imply that the considered iterative methods are efficient.     

On the Moore–Penrose inverse in solving saddle‐point systems with singular diagonal blocks

This paper deals with the role of the generalized inverses in solving saddle‐point systems arising naturally in the solution of many scientific and engineering problems when finite‐element tearing and interconnecting based domain decomposition methods are used to the numerical solution. It was shown that the Moore–Penrose inverse may be obtained in this case at negligible cost by projecting an arbitrary generalized inverse using orthogonal projectors. Applying an eigenvalue analysis based on the Moore–Penrose inverse, we proved that for simple model problems, the number of conjugate gradient iterations required for the solution of associate dual systems does not depend on discretization norms. The theoretical results were confirmed by numerical experiments with linear elasticity problems. Copyright © 2011 John Wiley & Sons, Ltd.     

Line integral methods which preserve all invariants of conservative problems

Recently, the class of Hamiltonian Boundary Value Methods (HBVMs) has been introduced with the aim of preserving the energy associated with polynomial Hamiltonian systems (and, more in general, with all suitably regular Hamiltonian systems). However, many interesting problems admit other invariants besides the Hamiltonian function. It would be therefore useful to have methods able to preserve any number of independent invariants. This goal is achieved by generalizing the line-integral approach which HBVMs rely on, thus obtaining a number of generalizations which we collectively name Line Integral Methods. In fact, it turns out that this approach is quite general, so that it can be applied to any numerical method whose discrete solution can be suitably associated with a polynomial, such as a collocation method, as well as to any conservative problem. In particular, a completely conservative variant of both HBVMs and Gauss collocation methods is presented. Numerical experiments confirm the effectiveness of the proposed methods.     

延迟积分微分方程线性多步方法的稳定性分析

考虑带常延迟的延迟积分微分方程线性系统零解的渐近稳定性，本文采用拉格朗日插值的线性多步方法，探讨了系统数值方法的线性稳定性。证明了所有A-稳定且强零-稳定的Pouzet型线性多步方法能够保持原线性系统的延迟不依赖稳定性。     

Numerical solution of the finite horizon stochastic linear quadratic control problem

The treatment of the stochastic linear quadratic optimal control problem with finite time horizon requires the solution of stochastic differential Riccati equations. We propose efficient numerical methods, which exploit the particular structure and can be applied for large‐scale systems. They are based on numerical methods for ordinary differential equations such as Rosenbrock methods, backward differentiation formulas, and splitting methods. The performance of our approach is tested in numerical experiments.     

Contributions to the development of differential systems exactly solved by multistep finite-difference schemes

The motivation underlying this contribution has been to complete some of the topics concerning exact schemes for numerically solving ordinary differential equations. A procedure for obtaining differential systems exactly solved by a given finite-difference method is described. Examples illustrating the application of the procedure for obtaining first-order, second-order and systems of differential equations exactly solved by different numerical methods are given. Among the numerical methods considered there are the Trapezoidal Rule, the two-step Adams-Bashforth method and the Numerov method. Some numerical examples are presented to provide evidence that the procedure works properly.     

Parameter Estimation with the Augmented Lagrangian Method for a Parabolic Equation

In this paper, we investigate the numerical identification of the diffusion parameters in a linear parabolic problem. The identification is formulated as a constrained minimization problem. By using the augmented Lagrangian method, the inverse problem is reduced to a coupled nonlinear algebraic system, which can be solved efficiently with the preconditioned conjugate gradient method. Finally, we present some numerical experiments to show the efficiency of the proposed methods, even for identifying highly discontinuous parameters.This work was partially supported by the Research Council of Norway, Grant NFR-128224/431.     

Planar Conjugate Gradient Algorithm for Large-Scale Unconstrained Optimization,Part 1: Theory

In this paper, we present a new conjugate gradient (CG) based algorithm in the class of planar conjugate gradient methods. These methods aim at solving systems of linear equations whose coefficient matrix is indefinite and nonsingular. This is the case where the application of the standard CG algorithm by Hestenes and Stiefel (Ref. 1) may fail, due to a possible division by zero. We give a complete proof of global convergence for a new planar method endowed with a general structure; furthermore, we describe some important features of our planar algorithm, which will be used within the optimization framework of the companion paper (Part 2, Ref. 2). Here, preliminary numerical results are reported.This work was supported by MIUR, FIRB Research Program on Large-Scale Nonlinear Optimization, Rome, ItalyThe author acknowledges Luigi Grippo and Stefano Lucidi, who contributed considerably to the elaboration of this paper. The exchange of experiences with Massimo Roma was a constant help in the investigation. The author expresses his gratitude to the Associate Editor and the referees for suggestions and corrections.     

An improved class of generalized Runge–Kutta methods for stiff problems. Part I: The scalar case

A new family of p-stage methods for the numerical integration of some scalar equations and systems of ODEs is proposed. These methods can be seen as a generalization of the explicit p-stage Runge–Kutta ones, while providing better order and stability results. We will show in this first part that, at the cost of losing linearity in the formulas, it is possible to obtain explicit A-stable and L-stable methods for the numerical integration of scalar autonomous ODEs. Scalar autonomous ODEs are of very little interest in current applications. However, be begin studying this kind of problems because most of the work can be easily extended to a more general situation. In fact, we will show in a second part (entitled ‘The separated system case'), that it is possible to generalize our methods so that they can be applied to some non-autonomous scalar ODEs and systems. We will obtain linearly implicit L-stable methods which do not require Jacobian evaluations. In both parts, some numerical examples are discussed in order to show the good performance of the new schemes.     

STABILITY OF GENERAL LINEAR METHODS FOR SYSTEMS OF FUNCTIONAL-DIFFERENTIAL AND FUNCTIONAL EQUATIONS

This paper is concerned with the numerical solution of functional-differential and func-tional equations which include functional-differential equations of neutral type as special cases. The adaptation of general linear methods is considered. It is proved that A-stable general linear methods can inherit the asymptotic stability of underlying linear systems.Some general results of numerical stability are also given.     

A General Family of Pseudo Two-step Runge-Kutta Methods

The aim of this paper is to design a new family of numerical methods of arbitrarily high order for systems of first-order differential equations which are to be termed pseudo two-step Runge-Kutta methods. By using collocation techniques, we can obtain an arbitrarily high-order stable pseudo two-step Runge-Kutta method with any desired number of implicit stages in retaining the two-step nature. In very first investigations, the pseudo two-step Runge-Kutta methods are shown to be promising numerical integration methods.AMS(MOS) subject classifications (1991) 65M12 65M20CR subject classifications G.1.7This work was partly supported by DAAD, N.R.P.F.S. and QG-96-02     

On quasi-Newton and pseudo-Newton algorithms

This paper surveys some of the existing approaches to quasi-Newton methods and introduces a new way for constructing inverse Hessian approximations for such algorithms. This new approach is based on restricting Newton's method to subspaces over which the inverse Hessian is assumed to be known, while expanding this subspace using gradient information. It is shown that this approach can lead to some well-known formulas for updating the inverse Hessian approximation. Deriving such updates through this approach provides new understanding of these formulas and their relation to the pseudo-Newton-Raphson algorithm.     

A COMPUTATIONAL APPROACH FOR OPTIMAL CONTROL SYSTEMS GOVERNED BY PARABOLIC VARIATIONAL INEQUALITIES

Iterative techniques for solving optimal control systems governed by parabolic variational inequalities are presented. The techniques we use are based on linear finite elements method to approximate the state equations and nonlinear conjugate gradient methods to solve the discrete optimal control problem. Convergence results and numerical experiments are presented.     

The generalized residual cutting method and its convergence characteristics

Iterative methods and especially Krylov subspace methods (KSM) are a very useful numerical tool in solving for large and sparse linear systems problems arising in science and engineering modeling. More recently, the nested loop KSM have been proposed that improve the convergence of the traditional KSM. In this article, we review the residual cutting (RC) and the generalized residual cutting (GRC) that are nested loop methods for large and sparse linear systems problems. We also show that GRC is a KSM that is equivalent to Orthomin with a variable preconditioning. We use the modified Gram–Schmidt method to derive a stable GRC algorithm. We show that GRC presents a general framework for constructing a class of “hybrid” (nested) KSM based on inner loop method selection. We conduct numerical experiments using nonsymmetric indefinite matrices from a widely used library of sparse matrices that validate the efficiency and the robustness of the proposed methods.     

Incremental Gradient Algorithms with Stepsizes Bounded Away from Zero

We consider the class of incremental gradient methods for minimizing a sum of continuously differentiable functions. An important novel feature of our analysis is that the stepsizes are kept bounded away from zero. We derive the first convergence results of any kind for this computationally important case. In particular, we show that a certain -approximate solution can be obtained and establish the linear dependence of on the stepsize limit. Incremental gradient methods are particularly well-suited for large neural network training problems where obtaining an approximate solution is typically sufficient and is often preferable to computing an exact solution. Thus, in the context of neural networks, the approach presented here is related to the principle of tolerant training. Our results justify numerous stepsize rules that were derived on the basis of extensive numerical experimentation but for which no theoretical analysis was previously available. In addition, convergence to (exact) stationary points is established when the gradient satisfies a certain growth property.     

Enhancing energy conserving methods

Recent observations [5] indicate that energy-momentum methods might be better suited for the numerical integration of highly oscillatory Hamiltonian systems than implicit symplectic methods. However, the popular energy-momentum method, suggested in [3], achieves conservation of energy by a global scaling of the force field. This leads to an undesirable coupling of all degrees of freedom that is not present in the original problem formulation. We suggest enhancing this energy-momentum method by splitting the force field and using separate adjustment factors for each force. In case that the potential energy function can be split into a strong and a weak part, we also show how to combine an energy conserving discretization of the strong forces with a symplectic discretization of the weak contributions. We demonstrate the numerical properties of our method by simulating particles that interact through Lennard-Jones potentials and by integrating the Sine-Gordon equation.This work was partly supported by NIH Grant P41RR05969, DOE/NSF Grant DE-FG02-91-ER25099/DMS-9304268, and NSF GCAG/HPCC ASC-9318159.     

Computational experience with methods for estimating sparse hessians for nonlinear optimization

The paper compares a factorized sparse quasi-Newton update of Goldfarb with a nonfactorized BFGS sparse update of Shanno on a series of test problems, with numerical results strongly favoring the unfactorized update. Analysis of Goldfarb's method is done to explain the poor numerical performance. Two specific conjugate gradient methods for solving the required systems of linear equations with the unfactorized update are described and tested.This research was supported by the National Science Foundation under Grant No. MCS-77-07327     

TWO NOVEL GRADIENT METHODS WITH OPTIMAL STEP SIZES

In this work we introduce two new Barzilai and Borwein-like steps sizes for the classical gradient method for strictly convex quadratic optimization problems.The proposed step sizes employ second-order information in order to obtain faster gradient-type methods.Both step sizes are derived from two unconstrained optimization models that involve approximate information of the Hessian of the objective function.A convergence analysis of the proposed algorithm is provided.Some numerical experiments are performed in order to compare the efficiency and effectiveness of the proposed methods with similar methods in the literature.Experimentally,it is observed that our proposals accelerate the gradient method at nearly no extra computational cost,which makes our proposal a good alternative to solve large-scale problems.