**Gidon Eshel491 HindsDept. of the Geophysical
Sciences,5734 S. Ellis Ave., The Univ. of Chicago,Chicago, IL
60637(773) 702-0440, geshel@midway.uchicago.edu
**

If we have a set of linearly-independent but non-orthogonal vectors , , we
often wish to turn them into an alternative set of vectors, , , that
are mutually orthonormal,

Achieving this orthonormalization is the purpose of the Gram-Schmidt procedure.

Let's address the particular example of

The first is simple, involving a simple normalization;

The second vector will be the original second vector, , minus
its projection on the first orthonormal basis vector, . That is,

which, upon normalization to unit norm, becomes

To construct the last basis vector, we need to subtract from its
projections on *both* and
;

Sparing you the straightforward yet tedious arithmetic,

which, when normalized, becomes

To test the results, we need to make sure that

as required.

In conclusion, we have transformed our original, non-orthonormal, basis set,

to an entirely equivalent set, whose vectors are all mutually orthogonal and have unit norms. Now, when we express an arbitrary vector from the space spanned by these 2 alternative sets as a linear combination of the orthonormal vectors , and , the projections (the weights) are entirely independent of one another, and their sum is exactly the norm of the represented vector, and not more. This is the reason orthonormalization of the basis vectors is very useful.