In this paper, we present an abstract framework which describes algebraically the derivation of order conditions independently of the nature of differential equations considered or the type of integrators used to solve them. Our structure includes a Hopf algebra of functions, whose properties are used to answer several questions of prime interest in numerical analysis. In particular, we show that, under some mild assumptions, there exist integrators of arbitrarily high orders for arbitrary (modified) vector fields.

In long-time numerical integration of Hamiltonian systems,and especially in molecular dynamics simulation,it is important that the energy is well conserved. For symplecticintegrators applied with sufficiently small step size, thisis guaranteed by the existence of a modifiedHamiltonian that is exactly conserved up to exponentially smallterms. This article is concerned with the simplifiedTakahashi-Imada method, which is a modificationof the Störmer-Verlet method that is as easy to implement buthas improved accuracy. This integrator is symmetric andvolume-preserving, but no longer symplectic. We study itslong-time energy conservation and give theoreticalarguments, supported by numerical experiments, whichshow the possibility of a drift in the energy (linear or like a random walk).With respect to energy conservation, this article provides empiricaland theoretical data concerning the importance of using a symplecticintegrator.

We show that while Runge-Kutta methods cannot preserve polynomial invariants in general, they can preserve polynomials that are the energy invariant of canonical Hamiltonian systems.

In this paper, we studythe linear Schrödinger equation over the d-dimensional torus,with small values of the perturbing potential.We consider numerical approximations of the associated solutions obtainedby a symplectic splitting method (to discretize the time variable) in combination with the Fast Fourier Transform algorithm (to discretize the space variable).In this fully discrete setting, we prove that the regularity of the initialdatum is preserved over long times, i.e. times that are exponentially longwith the time discretization parameter. We here refer to Gevrey regularity, and our estimatesturn out to be uniform in the space discretization parameter.This paper extends [G. Dujardin and E. Faou, Numer. Math.97 (2004) 493–535], where a similar result has been obtained inthe semi-discrete situation, i.e. when the mere time variable is discretized and spaceis kept a continuous variable.

The plane wave stability properties of the conservative schemes of Besse [SIAM J. Numer. Anal.42 (2004) 934–952] and Fei et al. [Appl. Math. Comput.71 (1995) 165–177] for the cubic Schrödinger equation are analysed. Although the two methods possess many of the same conservation properties, we show that their stability behaviour is very different.An energy preserving generalisation of the Fei method with improved stability is presented.

The integration to steady state of many initial value ODEs and PDEs using the forward Euler methodcan alternatively be considered as gradient descent for an associated minimization problem.Greedy algorithms such as steepest descent for determining the step size are asslow to reach steady state as is forward Euler integration with the best uniform step size.But other, much faster methods using bolder step size selection exist.Various alternatives are investigated from both theoretical and practical points of view.The steepest descent method is also known for the regularizing or smoothing effect that thefirst few steps have for certain inverse problems,amounting to a finite time regularization. We further investigate the retention of thisproperty using the faster gradient descent variants in the context of two applications.When the combination of regularization and accuracy demands more than a dozen or so steepestdescent steps, the alternatives offer an advantage, even though (indeed because)the absolute stability limit of forward Euler is carefully yet severely violated.

The numerical solution of ill-posed problems requires suitableregularization techniques. One possible option is to consider timeintegration methods to solve the Showalter differential equationnumerically. The stopping time of the numerical integrator correspondsto the regularization parameter. A number of well-knownregularization methods such as the Landweber iteration or theLevenberg-Marquardt method can be interpreted as variants of theEuler method for solving the Showalter differential equation. Motivated by an analysis of the regularization properties of theexact solution of this equation presented by [U. Tautenhahn, Inverse Problems10 (1994) 1405–1418], we consider a variant of the exponential Euler methodfor solving the Showalter ordinary differential equation. We discuss asuitable discrepancy principle for selecting the step sizes withinthe numerical method and we review the convergence properties of [U. Tautenhahn, Inverse Problems10 (1994) 1405–1418], and of our discrete version [M. Hochbruck et al., Technical Report (2008)].Finally, we present numerical experiments which show that thismethod can be efficiently implemented by using Krylov subspacemethods to approximate the product of a matrix function with avector.

Many complex systems occurring in various application share the property that theunderlying Markov process remains in certain regions of the state space for long times,and that transitions between such metastable sets occur only rarely.Often the dynamics within each metastable set is of minor importance, but the transitions between these sets are crucial for the behavior and the understanding of the system.Since simulations of the original process are usually prohibitively expensive, the effective dynamics of the system, i.e. the switching between metastable sets, has to be approximated in a reliable way.This is usually done by computing the dominant eigenvectors and eigenvalues of the transferoperator associated to the Markov process. In many real applications, however, the matrix representing the spatially discretized transfer operator can be extremely large, such that approximating eigenvectors and eigenvaluesis a computationally critical problem.In this article we present a novel method to determine the effective dynamics via the transfer operator without computing its dominant spectral elements. The main idea is that a time series of the process allows to approximate the sampling kernel of the process, which is an integral kernel closely related to the transition function of the transfer operator.Metastability is taken into account by representing the approximative sampling kernel by a linear combination of kernels each of which represents the process on one of the metastable sets.The effect of the approximation error on the dynamics of the system is discussed,and the potential of the new approach is illustrated by numerical examples.

We present a Monte Carlo technique for sampling from thecanonical distribution in molecular dynamics. The method is built uponthe Nosé-Hoover constant temperature formulation and the generalizedhybrid Monte Carlo method. In contrast to standard hybrid Monte Carlo methodsonly the thermostat degree of freedom is stochastically resampled during a Monte Carlo step.

In [C.W. Gear, T.J. Kaper, I.G. Kevrekidis and A. Zagaris,SIAM J. Appl. Dyn. Syst. 4 (2005) 711–732],we developeda class of iterative algorithmswithin the contextof equation-free methodsto approximatelow-dimensional,attracting,slow manifoldsin systemsof differential equationswith multiple time scales.For user-specified valuesof a finite numberof the observables,the mth memberof the classof algorithms( $m = 0, 1, \ldots$ )finds iterativelyan approximationof the appropriate zeroof the (m+1)st time derivativeof the remaining variablesanduses this rootto approximate the locationof the pointon the slow manifoldcorresponding to these valuesof the observables.This articleis the firstof two articlesin whichthe accuracy and convergenceof the iterative algorithmsare analyzed.Here,we work directlywith fast-slow systems,in which there isan explicit small parameter, ε,measuring the separationof time scales.We show that,for each $m = 0, 1, \ldots$ ,the fixed pointof the iterative algorithmapproximates the slow manifoldup to and includingterms of ${\mathcal O}(\varepsilon^m)$ .Moreover,for each m,we identify explicitlythe conditionsunder whichthe mth iterative algorithmconverges to this fixed point.Finally,we show thatwhenthe iterationis unstable(orconverges slowly)it may be stabilized(orits convergencemay be accelerated)by applicationof the Recursive Projection Method.Alternatively,the Newton-KrylovGeneralized Minimal Residual Methodmay be used.In the subsequent article,we will considerthe accuracy and convergenceof the iterative algorithmsfor a broader classof systems – in whichthere need not bean explicitsmall parameter – to whichthe algorithms also apply.

In this paper, we consider linear ordinary differential equations originating in electronic engineering, which exhibit exceedingly rapid oscillation. Moreover, the oscillation model is completely different from the familiar framework of asymptotic analysis of highly oscillatory integrals. Using a Bessel-function identity, we expand the oscillator into asymptotic series, and this allows us to extend Filon-type approach to this setting. The outcome is a time-stepping method that guarantees high accuracy regardless of the rate of oscillation.

This paper deals with modeling the passivebehavior of skeletal muscle tissue includingcertain microvibrations at the cell level. Our approach combines a continuum mechanics model with large deformation and incompressibility at the macroscale with chains of coupled nonlinear oscillators.The model verifies that an externally appliedvibration at the appropriate frequency is able to synchronize microvibrations in skeletal muscle cells.From the numerical analysis point of view, one faces here a partial differential-algebraic equation (PDAE) that after semi-discretization in space by finite elements possessesan index up to three, depending on certain physicalparameters. In this context, the consequences forthe time integration as well as possible remedies are discussed.