Numerical Shadowing Near Hyperbolic Trajectories

This is the published version, also available here: http://dx.doi.org/10.1137/0916068.Shadowing is a means of characterizing global errors in the numerical solution of initial value ordinary differential equations by allowing for a small perturbation in the initial condition. The method presented in this paper allows for a perturbation in the initial condition and a reparameterization of time in order to compute the shadowing distance in the neighborhood of a periodic orbit or more generally in the neighborhood of an attractor. The method is formulated for one-step methods and both a serial and parallel implementation are applied to the forced van der Pol equation, the Lorenz equation and to the approximation of a periodic orbit

On the Error in the Product QR Decomposition

This is the published version, also available here: http://dx.doi.org/10.1137/090761562.We develop both a normwise and a componentwise error analysis for the QR factorization of long products of invertible matrices. We obtain global error bounds for both the orthogonal and upper triangular factors that depend on uniform bounds on the size of the local error, the local degree of nonnormality, and integral separation, a natural condition related to gaps between eigenvalues but for products of matrices. We illustrate our analytical results with numerical results that show the dependence on the degree of nonnormality and the strength of integral separation

Numerical Shadowing Using Componentwise Bounds and a Sharper Fixed Point Result

This is the published version, also available here: http://dx.doi.org/10.1137/S1064827599353452.Shadowing provides a means of obtaining global error bounds for approximate solutions of nonlinear differential equations with interesting dynamics, in particular, for initial value problems with positive Lyapunov exponents. Shadowing breaks down in the presence of zero Lyapunov exponents, although some results such as shadowing with rescaling of time have been obtained. Using a reformulation of the original differential equations and an improved fixed point result we take advantage of componentwise local error bounds to use relatively smaller error tolerances in nonhyperbolic and contractive directions (i.e., in directions corresponding to zero and negative Lyapunov exponents). The result is a decrease in the shadowing global error

Nucleation and propagation of phase mixtures in a bistable chain

This item is copyrighted by the American physical society, and can be found electronically from http://journals.aps.org/prb/abstract/10.1103/PhysRevB.79.144123.We consider a prototypical discrete model of phase transitions. The model consists of a chain of particles, each interacting with its nearest and next-to-nearest neighbors. The long-range interaction between next-to-nearest neighbors is assumed to be harmonic, while the nearest-neighbor interactions are nonlinear and bistable. We consider overdamped dynamics of the chain and after suitable rescaling obtain a discrete reaction-diffusion equation with a negative diffusion coefficient. Using a biquadratic nearest-neighbor interaction potential and introducing new variables, we construct and study traveling-wave-like solutions that describe dynamics of phase mixtures in the lattice. Depending on the value of the applied force, phase mixtures either get trapped in one of the multiple equilibrium states or propagate through the chain at a constant speed. At low velocities near the depinning threshold, the motion is of stick-slip type. Numerical results for smoother potentials also suggest the existence and stability of the steady motion in a certain range of applied loads

On the Error in QR Integration

This is the published version, also available here: http://dx.doi.org/10.1137/06067818X.An important change of variables for a linear time varying system $\dot x=A(t)x, t\ge 0$, is that induced by the QR-factorization of the underlying fundamental matrix solution: $X=QR$, with Q orthogonal and R upper triangular (with positive diagonal). To find this change of variable, one needs to solve a nonlinear matrix differential equation for Q. Practically, this means finding a numerical approximation to Q by using some appropriate discretization scheme, whereby one attempts to control the local error during the integration. Our contribution in this work is to obtain global error bounds for the numerically computed Q. These bounds depend on the local error tolerance used to integrate for Q, and on structural properties of the problem itself, but not on the length of the interval over which we integrate. This is particularly important, since—in principle—Q may need to be found on the half-line $t\ge 0$