7,049 research outputs found
An iterative semi-implicit scheme with robust damping
An efficient, iterative semi-implicit (SI) numerical method for the time
integration of stiff wave systems is presented. Physics-based assumptions are
used to derive a convergent iterative formulation of the SI scheme which
enables the monitoring and control of the error introduced by the SI operator.
This iteration essentially turns a semi-implicit method into a fully implicit
method. Accuracy, rather than stability, determines the timestep. The scheme is
second-order accurate and shown to be equivalent to a simple preconditioning
method. We show how the diffusion operators can be handled so as to yield the
property of robust damping, i.e., dissipating the solution at all values of the
parameter \mathcal D\dt, where is a diffusion operator and \dt
the timestep. The overall scheme remains second-order accurate even if the
advection and diffusion operators do not commute. In the limit of no physical
dissipation, and for a linear test wave problem, the method is shown to be
symplectic. The method is tested on the problem of Kinetic Alfv\'en wave
mediated magnetic reconnection. A Fourier (pseudo-spectral) representation is
used. A 2-field gyrofluid model is used and an efficacious k-space SI operator
for this problem is demonstrated. CPU speed-up factors over a CFL-limited
explicit algorithm ranging from to several hundreds are obtained,
while accurately capturing the results of an explicit integration. Possible
extension of these results to a real-space (grid) discretization is discussed.Comment: Submitted to the Journal of Computational Physics. Clarifications and
caveats in response to referees, numerical demonstration of convergence rate,
generalized symplectic proo
A class of high-order Runge-Kutta-Chebyshev stability polynomials
The analytic form of a new class of factorized Runge-Kutta-Chebyshev (FRKC)
stability polynomials of arbitrary order is presented. Roots of FRKC
stability polynomials of degree are used to construct explicit schemes
comprising forward Euler stages with internal stability ensured through a
sequencing algorithm which limits the internal amplification factors to . The associated stability domain scales as along the real axis.
Marginally stable real-valued points on the interior of the stability domain
are removed via a prescribed damping procedure.
By construction, FRKC schemes meet all linear order conditions; for nonlinear
problems at orders above 2, complex splitting or Butcher series composition
methods are required. Linear order conditions of the FRKC stability polynomials
are verified at orders 2, 4, and 6 in numerical experiments. Comparative
studies with existing methods show the second-order unsplit FRKC2 scheme and
higher order (4 and 6) split FRKCs schemes are efficient for large moderately
stiff problems.Comment: 24 pages, 5 figures. Accepted for publication in Journal of
Computational Physics, 22 Jul 2015. Revise
A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options
We present a parallel algorithm for solving backward stochastic differential
equations (BSDEs in short) which are very useful theoretic tools to deal with
many financial problems ranging from option pricing option to risk management.
Our algorithm based on Gobet and Labart (2010) exploits the link between BSDEs
and non linear partial differential equations (PDEs in short) and hence enables
to solve high dimensional non linear PDEs. In this work, we apply it to the
pricing and hedging of American options in high dimensional local volatility
models, which remains very computationally demanding. We have tested our
algorithm up to dimension 10 on a cluster of 512 CPUs and we obtained linear
speedups which proves the scalability of our implementationComment: 25 page
Adaptive multiresolution schemes with local time stepping for two-dimensional degenerate reaction-diffusion systems
We present a fully adaptive multiresolution scheme for spatially
two-dimensional, possibly degenerate reaction-diffusion systems, focusing on
combustion models and models of pattern formation and chemotaxis in
mathematical biology. Solutions of these equations in these applications
exhibit steep gradients, and in the degenerate case, sharp fronts and
discontinuities. The multiresolution scheme is based on finite volume
discretizations with explicit time stepping. The multiresolution representation
of the solution is stored in a graded tree. By a thresholding procedure, namely
the elimination of leaves that are smaller than a threshold value, substantial
data compression and CPU time reduction is attained. The threshold value is
chosen optimally, in the sense that the total error of the adaptive scheme is
of the same slope as that of the reference finite volume scheme. Since chemical
reactions involve a large range of temporal scales, but are spatially well
localized (especially in the combustion model), a locally varying adaptive time
stepping strategy is applied. It turns out that local time stepping accelerates
the adaptive multiresolution method by a factor of two, while the error remains
controlled.Comment: 27 pages, 14 figure
Cumulative reports and publications through December 31, 1990
This document contains a complete list of ICASE reports. Since ICASE reports are intended to be preprints of articles that will appear in journals or conference proceedings, the published reference is included when it is available
A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options
We present a parallel algorithm for solving backward stochastic differential equations (BSDEs in short) which are very useful theoretic tools to deal with many financial problems ranging from option pricing option to risk management. Our algorithm based on Gobet and Labart (2010) exploits the link between BSDEs and non linear partial differential equations (PDEs in short) and hence enables to solve high dimensional non linear PDEs. In this work, we apply it to the pricing and hedging of American options in high dimensional local volatility models, which remains very computationally demanding. We have tested our algorithm up to dimension 10 on a cluster of 512 CPUs and we obtained linear speedups which proves the scalability of our implementationbackward stochastic differential equations, parallel computing, Monte- Carlo methods, non linear PDE, American options, local volatility model.
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