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M. P. Calvo

Publications and source records attributed to M. P. Calvo.

5 recordsLinked to original sources

Stroboscopic averaging methods to study autoresonance and other problems with slowly varying forcing frequencies

Autoresonance is a phenomenon of physical interest that may take place when a nonlinear oscillator is forced at a frequency that varies slowly. The stroboscopic averaging method (SAM), which provides an efficient numerical technique for the integration of highly oscillatory systems, cannot be used directly to study autoresonance due to the slow changes of the forcing frequency. We study how to modify SAM to cater for such slow variations. Numerical experiments show the computational advantages of using SAM.

math.NA

Taylor-Fourier approximation

In this paper, we introduce an algorithm that provides approximate solutions to semi-linear ordinary differential equations with highly oscillatory solutions, which, after an appropriate change of variables, can be rewritten as non-autonomous systems with a $(2\pi/\omega)$-periodic dependence on $t$. The proposed approximate solutions are given in closed form as functions $X(\omega t,t)$, where $X(\theta,t)$ is (i) a truncated Fourier series in $\theta$ for fixed $t$ and (ii) a truncated Taylor series in $t$ for fixed $\theta$, which motivates the name of the method. These approximations are uniformly accurate in $\omega$, meaning that their accuracy does not degrade as $\omega \to \infty$. In addition, Taylor-Fourier approximations enable the computation of high-order averaging equations for the original semi-linear system, as well as related maps that are particularly useful in the highly oscillatory regime (i.e., for sufficiently large $\omega$). The main goal of this paper is to develop an efficient procedure for computing such approximations by combining truncated power series arithmetic with the Fast Fourier Transform (FFT). We present numerical experiments that illustrate the effectiveness of the proposed method, including applications to the nonlinear Schr\"odinger equation with non-smooth initial data and a perturbed Kepler problem from satellite orbit dynamics.

math.NA

Symmetrically processed splitting integrators for enhanced Hamiltonian Monte Carlo sampling

We construct integrators to be used in Hamiltonian (or Hybrid) Monte Carlo sampling. The new integrators are easily implementable and, for a given computational budget, may deliver five times as many accepted proposals as standard leapfrog/Verlet without impairing in any way the quality of the samples. They are based on a suitable modification of the processing technique first introduced by J.C. Butcher. The idea of modified processing may also be useful for other purposes, like the construction of high-order splitting integrators with positive coefficients.

math.NA

HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate

The leapfrog integrator is routinely used within the Hamiltonian Monte Carlo method and its variants. We give strong numerical evidence that alternative, easy to implement algorithms yield fewer rejections with a given computational effort. When the dimensionality of the target distribution is high, the number of accepted proposals may be multiplied by a factor of three or more. This increase in the number of accepted proposals is not achieved by impairing any positive features of the sampling. We also establish new non-asymptotic and asymptotic results on the monotonic relationship between the expected acceptance rate and the expected energy error. These results further validate the derivation of one of the integrators we consider and are of independent interest.

stat.CO