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Bulirsch-Stoer

version 1.0.0.0 (3.3 KB) by Matthew Kelly
Solves an initial value problem. Good for very accurate solutions for smooth systems.

3 Downloads

Updated 20 Feb 2016

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% [z, info] = BulirschStoer(dynFun,t,z0,tol)
%
% Solves an initial value problem using the Bulirsch-Stoer method. This
% method is ideal for high-accuracy solutions to smooth initial value
% problems.
%
% Computes z(t) such that dz/dt = dynFun(t,z), starting from the initial
% state z0. The solution at the grid-points will be accurate to within tol.
%
% If the provided grid is insufficient, this function will automatically
% introduce intermediate grid points to achieve the required accuracy.
%
% INPUTS:
% dynFun = function handle for the system dynamics
% dz = dynFun(t,z)
% t = scalar time
% z = [nz,1] = state as column vector
% dz = [nz,1] = derivative of state as column vector
% t = [1,nt] = time grid-point vector
% z0 = [nz,1] = initial state vector
% tol = [nz,1] = error tolerance along each dimension. If tol is a
% scalar, then all dimensions will satisfy that error tolerance.
%
% OUTPUTS: (nt = n+1)
% z = [nz,nt] = solution to the initial value problem
% info
% .error = [nx,nt] = error estimate at each grid point
% .nFunEval = [1,nt] = number of function evaluations for each point
%
% NOTES:
% Implementation details:
% http://web.mit.edu/ehliu/Public/Spring2006/18.304/implementation_bulirsch_stoer.pdf
%

Cite As

Matthew Kelly (2020). Bulirsch-Stoer (https://www.mathworks.com/matlabcentral/fileexchange/55528-bulirsch-stoer), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (4)

Matthew Kelly

How to speed up this computation? It depends on your goals...
- If you want high accuracy (better than 1e-9 or so), then this is likely the best method. If you want an intermediate accuracy, say 1e-6, then you might get faster results with ode45. Let's assume that you want high accuracy, so will stick with this method.
- How to make the code to be faster? Use the Matlab profiler - it is really good. Look for functions or lines that take a long time and that can be modified easily.
- Check your function: is it well vectorized? The profiler might give some suggestions as well.
- Are your dynamics smooth? If not, then this method will take a long time to converge. If there are known discontinuities, then you need to break up the calculation into chunks that are smooth.
- There is some rough memory allocation at the start of this function, based on `nRefineMax`. I doubt it is the bottle neck in the code, but you might be able to tweak that for a small improvement.
- The lines marked "Compute the extrapolation table entries" might also be able to be cleaned up, if they are too slow.

Good luck!

ryan comeau

Works well. set the tolerance to 1e-10 and decreased the computation time by a little bit. I have a 42+ dimension vector and I'm wondering if there's a way I can speed this up further?

Udayan Banerjee

Jonathan Deng

MATLAB Release Compatibility
Created with R2012a
Compatible with any release
Platform Compatibility
Windows macOS Linux