9,677 research outputs found
Implicit-Explicit Runge-Kutta schemes for numerical discretization of optimal control problems
Implicit-explicit (IMEX) Runge-Kutta methods play a major rule in the
numerical treatment of differential systems governed by stiff and non-stiff
terms. This paper discusses order conditions and symplecticity properties of a
class of IMEX Runge-Kutta methods in the context of optimal control problems.
The analysis of the schemes is based on the continuous optimality system. Using
suitable transformations of the adjoint equation, order conditions up to order
three are proven as well as the relation between adjoint schemes obtained
through different transformations is investigated. Conditions for the IMEX
Runge-Kutta methods to be symplectic are also derived. A numerical example
illustrating the theoretical properties is presented
Spatially partitioned embedded Runge-Kutta Methods
We study spatially partitioned embedded RungeāKutta (SPERK) schemes for partial differential equations (PDEs), in which each of the component schemes is applied over a different part of the spatial domain. Such methods may be convenient for problems in which the smoothness of the solution or the magnitudes of the PDE coefficients vary strongly in space. We focus on embedded partitioned methods as they offer greater efficiency and avoid the order reduction that may occur in non-embedded schemes. We demonstrate that the lack of conservation in partitioned schemes can lead to non-physical effects and propose conservative additive schemes based on partitioning the fluxes rather than the ordinary differential equations. A variety of SPERK schemes are presented, including an embedded pair suitable for the time evolution of fifth-order weighted non-oscillatory (WENO) spatial discretizations. Numerical experiments are provided to support the theory
Another approach to Runge-Kutta methods
The condition equations are derived by the introduction of a system of equivalent differential equations, avoiding the usual formalism with trees and elementary differentials. Solutions to the condition equations are found by direct optimization, avoiding the necessity to introduce simplifying assumptions upon the Runge-Kutta coefficients. More favourable coefficients, in view of rounding errors, are found
Effective order strong stability preserving RungeāKutta methods
We apply the concept of effective order to strong stability preserving (SSP) explicit RungeāKutta methods. Relative to classical RungeāKutta methods, effective order methods are designed to satisfy a relaxed set of order conditions, but yield higher order accuracy when composed with special starting and stopping methods. The relaxed order conditions allow for greater freedom in the design of effective order methods. We show that this allows the construction of four-stage SSP methods with effective order four (such methods cannot have classical order four). However, we also prove that effective order five methodsālike classical order five methodsārequire the use of non-positive weights and so cannot be SSP. By numerical optimization, we construct explicit SSP RungeāKutta methods up to effective order four and establish the optimality of many of them. Numerical experiments demonstrate the validity of these methods in practice
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