2.1: Orthogonal vectors and subspaces

Vectors are easier to understand when they're described in terms of orthogonal bases. In addition, many of the spaces we're interested in are orthogonal to each other.

If A is a rectangular matrix, Ax = b is often unsolvable. The matrix ATA will help us find a vector that comes as close as possible to solving Ax = b.