| title | tags | authors | affiliations | date | bibliography | |||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
`RK-Opt`: A package for the design of numerical ODE solvers |
|
|
|
9 July 2020 |
paper.bib |
Ordinary and partial differential equations (ODEs and PDEs) are used to model
many important phenomena. In most cases, solutions of these models must be
approximated by numerical methods. Most of the relevant algorithms fall within
a few classes of methods, with the properties of individual methods determined
by their coefficients. The choice of appropriate coefficients in the design of
methods for specific applications is an important area of research.
RK-Opt is a software package for designing numerical ODE solvers with
coefficients optimally chosen to provide desired properties.
It is available from https://github.com/ketch/RK-Opt, with documentation
at http://numerics.kaust.edu.sa/RK-Opt/.
The primary focus of the package is on the design of Runge-Kutta methods, but
some routines for designing other classes of methods such as multistep
Runge-Kutta and general linear methods are also included.
Over the last several decades, a great deal of work has gone into the design of numerical ODE solvers. Initially this work was aimed at developing general purpose solvers, but over time the emphasis shifted increasingly toward development of optimized methods for specific applications. Different accuracy, stability, performance, and other properties may be relevant or essential depending on the nature of the equations to be solved.
RK-Opt provides code that can enforce desired properties and/or objective
functions. The constraints and objective are then used within an optimization
framework, to determine coefficients of methods that best achieve the desired
goal. Thus, RK-Opt is a sort of meta-software, consisting of algorithms whose
purpose is to create other algorithms.
Typically, the most obvious formulation of the corresponding optimization problem is intractable. Therefore, these problems are reformulated in ways that make them amenable to available techniques. These reformulations include, for instance, turning a nonconvex problem into a sequence of convex problems or even linear programs. The resulting algorithms can often guarantee optimality of their output. However, for the general problem of determining Runge-Kutta coefficients, the nonconvex problem must be attacked directly and optimality cannot be guaranteed.
RK-Opt is written entirely in MATLAB, and leverages the MATLAB Optimization
Toolbox as well as the Global Optimization Toolbox.
Its development has been motivated largely by research needs and
it has been used in a number of papers (see below).
RK-Opt includes the following subpackages.
This package computes optimal stability functions for Runge-Kutta methods. Here optimal means that the stable step size is maximized for a given ODE spectrum. The corresponding optimization problem is intractable under a direct implementation. The package uses the algorithm developed in [@2012_optimal_stability_polynomials], which transforms the problem into a sequence of convex problems and typically yields a solution in a few seconds. It relies on CVX [@cvx;@gb08] to solve the convex subproblems. This package is usually used as the first step in designing a Runge-Kutta method.
This package computes optimal Runge-Kutta coefficients based on a desired set of constraints and an objective. Available constraints include:
- The number of stages and order of accuracy
- The class of method (explicit, implicit, diagonally implicit, low-storage)
- The coefficients of the stability polynomial (usually determined using
polyopt)
Two objective functions are provided; methods can be optimized for the strong stability preserving (SSP) coefficient or the principal error norm (a measure of the leading-order truncation error coefficients). In addition to standard Runge-Kutta methods, various classes of multistep Runge-Kutta methods can also be optimized.
The optimization problem in question is highly nonconvex and the available solvers may fail to find a solution, or may converge to a non-optimal solution. For this reason, the implementation is based on solving many local optimization problems in parallel from different random initial points, using MATLAB's Global Optimization Toolbox.
The packages dwrk-opt and low-storage are specialized but less full-featured
versions of RK-Coeff-Opt that were developed for specific research projects
involving downwind Runge-Kutta methods and low-storage Runge-Kutta methods, respectively.
Whereas the previous two subpackages are fairly general-purpose tools, this package solves a very specific and discrete set of problems described in [@2009_monotonicity]. Specifically, the provided routines determine the coefficients of multistep methods (including classes of general linear methods) with the largest possible SSP coefficient (also known as radius of absolute monotonicity). The corresponding optimization problem had previously been attacked using brute force search, but this limited its solvability to methods with very few steps. In this package the problem is reformulated as a sequence of linear programming problems, enabling its efficient solution for methods with many steps.
RK-Opt development has proceeded in close connection to the NodePy package (https://github.com/ketch/NodePy).
Whereas RK-Opt is focused on the design of numerical methods, NodePy is focused
more on their analysis. A common workflow involves generating new methods with
RK-Opt and then studying their properties in more detail using NodePy.
Some of the research projects that have made use of RK-Opt include development of:
- SSP Runge-Kutta methods [@2008_explicit_ssp;@2009_implicit_ssp;@gottlieb2015optimal]
- SSP linear multistep methods [@2009_monotonicity]
- SSP general linear methods [@2011_tsrk;@2017_msrk]
- SSP IMEX Runge-Kutta methods [@conde2017implicit]
- Low-storage Runge-Kutta methods [@2010_LSRK]
- Optimal Runge-Kutta stability polynomials [@2012_optimal_stability_polynomials]
- Additive and downwind SSP Runge-Kutta methods [@2011_dwssp;@2018_perturbations]
- Optimal Runge-Kutta methods for specific PDE semi-discretizations [@parsani-eccomas;@Parsani_finnish;@2013_sd_erk;@2014_ssp_rkdg]
- Optimal Runge-Kutta methods for pseudo-time stepping [@vermeire2019optimal;@vermeire2020optimal]
- Embedded pairs for Runge-Kutta methods [@conde2018embedded]
- Runge-Kutta methods with high weak stage order [@2018_wso]
- SSP multistage, multiderivative methods [@christlieb2016explicit;@grant2019strong;@reynoso2017strong]
As can be seen from this list, applications have mostly stemmed from the work of the main developer's research group, but have since expanded beyond that.
Much of the initial RK-Opt development was performed by D. Ketcheson while
he was supported by a DOE Computational Science Graduate Fellowship. Development
has also been supported by funding from King Abdullah University of Science and Technology.