Automatic Differentiation with Matlab Objects
Automatic differentiation is a technique for computing the derivatives of a function using the chain rule. Matlab objects make it easy to implement automatic differentiation. Note that this package is implemented in a rather old version of Matlab. You may need to edit it for newer versions.
An example of using automatic differentiation to compute the value and derivative of the Rosenbrock function at the point [1,2] is as follows:
x=adiff([1,2]); % create the automatic differentiation object at [1,2]
rosen = 100*(x(1)^2-x(2))^2+(x(1)-1)^2; % compute rosenbrock func.
[x,dx] = adiffget(x); % retrieve the value x and derivative dx
Then x = 100 and dx = [-400,200].
The adiff object includes a helper function to convert any optimization without derivatives into one with derivatives. For example, if you have a function f which you wish to optimize, but it doesn't compute derivatives, it is usually enough to call
fminunc('autodiff',x0,options,'f',...)
The zip file includes a pdf help file.
Cite As
William McIlhagga (2024). Automatic Differentiation with Matlab Objects (https://www.mathworks.com/matlabcentral/fileexchange/26807-automatic-differentiation-with-matlab-objects), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.