Numerical modeling brings special considerations to traditional linear algebra. In particular, numerical methods find themselves constantly querying the size of error in an estimate. This requires the concept of size of vectors and matrices. This is not to imply that linear algebra misses this entirely. Most every linear algebra book defines the norm of vectors. Since the use of measures of size is more closely associated with analysis, the book will likely do little with the norm other than provide a few simple examples. Furthermore, most undergraduate linear algebra books do not address norms of matrices. This again is an important topic for numerical modelers as the success of our methods often hinge on size of a matrix norms.
These concepts are discuss in these notes.