Explanation of L2 norm of an error

When solving for the sparsest possible set of DEs, it is likely our found model will not describe the data exactly - there will be an error

Therefore we can measure the error and give the user it's $l^2$-norm

When working with 2 or more dimensional data, the $l^2$-norm returned will be vector of $l^2$ norms in each coordinate


Revision #1
Created 28 December 2022 15:28:21 by Sceptri
Updated 28 December 2022 15:29:10 by Sceptri