A modified predictor–corrector scheme for the two-dimensional sine-Gordon equation

A three-time level finite-difference scheme based on a fourth order in time and second order in space approximation has been proposed for the numerical solution of the nonlinear two-dimensional sine-Gordon equation. The method, which is analysed for local truncation error and stability, leads to the solution of a nonlinear system. To avoid solving it, a predictor–corrector scheme using as predictor a second-order explicit scheme is proposed. The procedure of the corrector has been modified by considering as known the already evaluated corrected values instead of the predictor ones. This modified scheme has been tested on the line and circular ring soliton and the numerical experiments have proved that there is an improvement in the accuracy over the standard predictor–corrector implementation. This research was co-funded by E.U. (75%) and by the Greek Government (25%).     

Variable stepsize implementation of multistep methods for y″=f(x,y,y′)

The Falkner method is a multistep scheme intended for the numerical solution of second-order initial value problems where the first derivative does appear explicitly. In this paper, we develop a procedure to obtain k-step Falkner methods (explicit and implicit) in their variable step-size versions, providing recurrence formulas to compute the coefficients efficiently. Considering a pair of explicit and implicit formulae, these may be implemented in predictor–corrector mode.     

A numerical method for the one‐dimensional sine‐Gordon equation

A numerical method based on a predictor–corrector (P‐C) scheme arising from the use of rational approximants of order 3 to the matrix‐exponential term in a three‐time level recurrence relation is applied successfully to the one‐dimensional sine‐Gordon equation, already known from the bibliography. In this P‐C scheme a modification in the corrector (MPC) has been proposed according to which the already evaluated corrected values are considered. The method, which uses as predictor an explicit finite‐difference scheme arising from the second order rational approximant and as corrector an implicit one, has been tested numerically on the single and the soliton doublets. Both the predictor and the corrector schemes are analyzed for local truncation error and stability. From the investigation of the numerical results and the comparison of them with other ones known from the bibliography it has been derived that the proposed P‐C/MPC schemes at least coincide in terms of accuracy with them. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2008     

An {O(\sqrt{n}L)} iteration primal-dual second-order corrector algorithm for linear programming

In this paper, we propose a primal-dual second-order corrector interior point algorithm for linear programming problems. At each iteration, the method computes a corrector direction in addition to the Ai–Zhang direction [Ai and Zhang in SIAM J Optim 16:400–417 (2005)], in an attempt to improve performance. The corrector is multiplied by the square of the step-size in the expression of the new iterate. We prove that the use of the corrector step does not cause any loss in the worst-case complexity of the algorithm. To our best knowledge, this is the first wide neighborhood second-order corrector algorithm enjoyed the low iteration bound of O(?nL){O(\sqrt{n}L)}, the same as the best known complexity results for interior point methods.     

On the numerical solution of the Klein‐Gordon equation

A predictor–corrector (P–C) scheme based on the use of rational approximants of second‐order to the matrix‐exponential term in a three‐time level reccurence relation is applied to the nonlinear Klein‐Gordon equation. This scheme is accelerated by using a modification (MPC) in which the already evaluated values are used for the corrector. Both the predictor and the corrector scheme are analyzed for local truncation error and stability. The proposed method is applied to problems possessing periodic, kinks and single, double‐soliton waves. The accuracy as well as the long time behavior of the proposed scheme is discussed. © 2008 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2009     

A second order numerical scheme for the solution of the one-dimensional Boussinesq equation

A predictor–corrector (P-C) scheme is applied successfully to a nonlinear method arising from the use of rational approximants to the matrix-exponential term in a three-time level recurrence relation. The resulting nonlinear finite-difference scheme, which is analyzed for local truncation error and stability, is solved using a P-C scheme, in which the predictor and the corrector are explicit schemes of order 2. This scheme is accelerated by using a modification (MPC) in which the already evaluated values are used for the corrector. The behaviour of the P-C/MPC schemes is tested numerically on the Boussinesq equation already known from the bibliography free of boundary conditions. The numerical results are derived for both the bad and the good Boussinesq equation and conclusions from the relevant known results are derived.      

A modified predictor-corrector method for linear programming

In the predictor-corrector method of Mizuno, Todd and Ye [1], the duality gap is reduced only at the predictor step and is kept unchanged during the corrector step. In this paper, we modify the corrector step so that the duality gap is reduced by a constant fraction, while the predictor step remains unchanged. It is shown that this modified predictor-corrector method retains the iteration complexity as well as the local quadratic convergence property.     

The solution of the two-dimensional sine-Gordon equation using the method of lines

The method of lines is used to transform the initial/boundary-value problem associated with the two-dimensional sine-Gordon equation in two space variables into a second-order initial-value problem. The finite-difference methods are developed by replacing the matrix-exponential term in a recurrence relation with rational approximants. The resulting finite-difference methods are analyzed for local truncation error, stability and convergence. To avoid solving the nonlinear system a predictor–corrector scheme using the explicit method as predictor and the implicit as corrector is applied. Numerical solutions for cases involving the most known from the bibliography line and ring solitons are given.     

Modelling and analysis of the non-iterative coupling process for co-simulation

Concerning non-iterative co-simulation, stepwise extrapolation of coupling signals is required to solve an overall system of interconnected subsystems. Each extrapolation is some kind of estimation and is directly associated with an estimation error. The introduced disturbance depends significantly on the macro-step size, i.e. the coupling step size, and influences the entire system behaviour. In addition, for synchronization purposes, sampling of the coupling signals can cause aliasing. Instead of analysing the coupling effects in the time domain, as it is commonly practised, we concentrate on a model-based approach to gain more insight into the coupling process. In this work, we consider commonly used polynomial extrapolation techniques and analyse them in the frequency domain. Based on this system-oriented point of view of the coupling process, a relation between the coupling signals and the macro-step size is available. In accordance to the dynamics of the interconnected subsystems, the model-based relation is used to select the most critical parameter, i.e. the macro-step size. Besides a ‘rule of thumb’ for meaningful step-size selection, a co-simulation benchmark example describing a two degree of freedom (2-DOF) mechanical system is used to demonstrate the advantages of modelling and the efficiency of the proposed method.     

A predictor–corrector scheme for the improved Boussinesq equation

An implicit finite-difference method based on rational approximants of second order to the matrix-exponential term in a three-time level recurrence relation has been proposed for the numerical solution of the improved Boussinesq equation already known from the bibliography. The method, which is analyzed for local truncation error and stability, leads to the solution of a nonlinear system. To overcome this difficulty a predictor–corrector (P–C) scheme in which the predictor is also a second order implicit one is proposed. The efficiency of the proposed method is tested to various wave packets and the results arising from the experiments are compared with the relevant ones known in the bibliography.     

A variable step-size control algorithm for the weak approximation of stochastic differential equations

In this paper, we propose two local error estimates based on drift and diffusion terms of the stochastic differential equations in order to determine the optimal step-size for the next stage in an adaptive variable step-size algorithm. These local error estimates are based on the weak approximation solution of stochastic differential equations with one-dimensional and multi-dimensional Wiener processes. Numerical experiments are presented to illustrate the effectiveness of this approach in the weak approximation of several standard test problems including SDEs with small noise and scalar and multi-dimensional Wiener processes.     

Error analysis for co-simulation with force-displacement coupling

Co-simulation is a simulation technique for time dependent coupled problems in engineering that restricts the data exchange between subsystems to discrete communication points in time. In the present paper we follow the block-oriented framework in the recently established industrial interface standard FMI for Model Exchange and Co-Simulation v2.0 and study local and global error of co-simulation algorithms for systems with force-displacement coupling. A rather general convergence result for the co-simulation of coupled systems without algebraic loops shows zero-stability of co-simulation algorithms with force-displacement coupling and proves that order reduction of local errors does not affect the order of global errors. The theoretical investigations are illustrated by numerical tests in the novel FMI-compatible co-simulation environment SNiMoWrapper . (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A new predictor–corrector method for solving unconstrained minimization problems

This paper presents a new predictor–corrector method for finding a local minimum of a twice continuously differentiable function. The method successively constructs an approximation to the solution curve and determines a predictor on it using a technique similar to that used in trust region methods for unconstrained optimization. The proposed predictor is expected to be more effective than Euler's predictor in the sense that the former is usually much closer to the solution curve than the latter for the same step size. Results of numerical experiments are reported to demonstrate the effectiveness of the proposed method.     

Runge–Kutta–Nyström‐type parallel block predictor–corrector methods

This paper describes the construction of block predictor–corrector methods based on Runge–Kutta–Nyström correctors. Our approach is to apply the predictor–corrector method not only with stepsize h, but, in addition (and simultaneously) with stepsizes a _i h, i = 1 ...,r. In this way, at each step, a whole block of approximations to the exact solution at off‐step points is computed. In the next step, these approximations are used to obtain a high‐order predictor formula using Lagrange or Hermite interpolation. Since the block approximations at the off‐step points can be computed in parallel, the sequential costs of these block predictor–corrector methods are comparable with those of a conventional predictor–corrector method. Furthermore, by using Runge–Kutta–Nyström corrector methods, the computation of the approximation at each off‐step point is also highly parallel. Numerical comparisons on a shared memory computer show the efficiency of the methods for problems with expensive function evaluations.     

Robust continuation methods for tracing solution curves of parameterized systems

The continuation methods are efficient methods to trace solution curves of nonlinear systems with parameters, which are common in many fields of science and engineering. Existing continuation methods are unstable for some complicated cases in practice, such as the case that solution curves are close to each other or the case that the curve turns acutely at some points. In this paper, a more robust corrector strategy—sphere corrector is presented. Using this new strategy, combining various predictor strategies and various iterative methods with local quadratic or superlinear convergence rates, robust continuation procedures for tracing curves are given. When the predictor steplength is no more than the so-called granularity of solution curves, our procedure of tracing solution curve can avoid “curve-jumping” and trace the whole solution curve successfully. Numerical experiments illustrate our method is more robust and efficient than the existing continuation methods.     

A New Infeasible Mehrotra-Type Predictor–Corrector Algorithm for Nonlinear Complementarity Problems Over Symmetric Cones

This paper establishes a theoretical framework of infeasible Mehrotra-type predictor–corrector algorithm for nonmonotone nonlinear complementarity problems over symmetric cones which can be regarded as an extension the Mehrotra’s algorithm proposed by Salahi et al. (On Mehrotra-type predictor–corrector algorithms. SIAM J Optim 18(4):1377–1397, 2005) from nonnegative orthant to symmetric cone. The iteration complexity of the algorithm is estimated, and some numerical results are provided. The numerical results show that the algorithm is efficient and reliable.     

Further Developments in an Iterative Projection and Contraction Method for Linear Programming

A linear programming problem can be translated into an equivalent general linear complementarity problem, which can be solved by an iterative projection and contraction (PC) method [6]. The PC method requires only two matrix-vector multiplications at each iteration and the efficiency in practice usually depends on the sparsity of the constraint-matrix. The prime PC algorithm in [6] is globally convergent; however, no statement can be made about the rate of convergence. Although a variant of the PC algorithm with constant step-size for linear programming [7] has a linear speed of convergence, it converges much slower in practice than the prime method [6]. In this paper, we develop a new step-size rule for the PC algorithm for linear programming such that the resulting algorithm is globally linearly convergent. We present some numerical experiments to indicate that it also works better in practice than the prime algorithm.     

A C0 interior penalty discontinuous Galerkin method for fourth‐order total variation flow. I: Derivation of the method and numerical results

We consider the numerical solution of a fourth‐order total variation flow problem representing surface relaxation below the roughening temperature. Based on a regularization and scaling of the nonlinear fourth‐order parabolic equation, we perform an implicit discretization in time and a C⁰ Interior Penalty Discontinuous Galerkin (C⁰IPDG) discretization in space. The C⁰IPDG approximation can be derived from a mixed formulation involving numerical flux functions where an appropriate choice of the flux functions allows to eliminate the discrete dual variable. The fully discrete problem can be interpreted as a parameter dependent nonlinear system with the discrete time as a parameter. It is solved by a predictor corrector continuation strategy featuring an adaptive choice of the time step sizes. A documentation of numerical results is provided illustrating the performance of the C⁰IPDG method and the predictor corrector continuation strategy. The existence and uniqueness of a solution of the C⁰IPDG method will be shown in the second part of this paper.     

A unified approach for elasto‐plastic FEM simulations,incorporating adaptive spatial and time discretization

In FEM calculations the discretization should be chosen in a way, that further mesh refinement does not change the results. Otherwise the discretization error might yield unphysically stiff material behavior. If elasto‐plasticity is considered, a second type of error due to the time discretization of the evolution equations has to be taken into account. Due to the non‐linearity of the underlying initial boundary value problem, large time increments often result in non‐converging solutions during equilibrium iteration. In our approach the time integration error resulting from a second order BDF2 time integration method is calculated and utilized in an automatic step size control. In conjunction with a DAE‐treatment of the initial boundary value problem, it allows in the average considerably larger time steps compared to classical ‘elastic predictor – plastic corrector’ schemes. To reduce the discretization error, adaptive mesh‐refinement based on a Z²‐error‐estimator is performed. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Nordsieck representation of DIMSIMs

A new representation for diagonally implicit multistage integration methods (DIMSIMs) is derived in which the vector of external stages directly approximates the Nordsieck vector. The methods in this formulation are zero-stable for any choice of variable mesh. They are also easy to implement since changing step-size corresponds to a simple rescaling of the vector of external approximations. The paper contains an analysis of local truncation error and of error accumulation in a variable step-size situation. This revised version was published online in June 2006 with corrections to the Cover Date.