The most common way to represent goemetric algebra multivectors (including rotors) in a computer is via an array of coefficients, one for each canonical form algebra basis element (canonical basis blade) ie. for a 4D basis space you will have a 2^4 dimensional algebra and have 2^4 dimensional array of coefficients. An alternate but probably faster way to represent them is with a dynamically resizing list with each element containing an index to a blade and the coefficient of the associated blade. In this case the multiplication of two multivectors will only use non zero basis blades and so should be algorithmically cheaper and lighter on memory usage.
In terms of practical usage I found the easiest place to get started with playing around with geometric algebra is probably in python with https://github.com/pygae/clifford . Full disclaimer I use this library daily and contribute to it extensively. This library uses the flat array of coefficients approach. With this python library you can apply 4D rotors via the sandwich product and do reversion (inversion of a rotor) via the tilde operator:
# Create a 4D geometric algebra with euclidean metric from clifford.g4 import * # Create a rotor R = layout.randomRotor() # Create a vector to rotate V = layout.randomV() # Apply the rotor to the vector V2 = R*V*~R
The specific definition of the geometric product and reverse for multivectors from an N-dimensional geometric algebra can be found in Chapter 4 of Geometric algebra for Physicists by Chris Doran and Anthony Lasenby.
A good C++ GA reference implementation for N-dimensional GAs using the list of elements approach can be found in Leo Dorst’s book Geometric Algebra for Physicists or on his website:
http://www.geometricalgebra.net/code.html . In general this is a great resource for GA, especially the conformal model and numerical implementations and concerns.