The Gram-Schmidt process is a means for converting a set of linearly independent vectors into a set of orthonormal vectors. If the set of vectors spans the ambient vector space, then this produces an orthonormal basis for the vector space.
The Gram-Schmidt process is a recursive procedure. After the first
vectors have been converted into
orthonormal vectors, the difference between the
original vector and its projection onto the space spanned by the first
orthonormal vectors is normalized to obtain the
vector in the orthonormal collection.
In two dimensions, start with a vector
and normalize it to obtain
. Next, project
onto
and compute
, the difference between
and this projection. Finally, normalize this vector to obtain
.
Browse all topics
