### Gram-Schmidt Orthogonalization

Recall from the end of §5.10 above that an
*orthonormal* set of vectors is a set of *unit-length*
vectors that are mutually *orthogonal*. In other words,
orthonormal vector set is just an orthogonal vector set in which each
vector has been normalized to unit length
.

**Theorem: **Given a set of linearly independent vectors
from , we can construct an
*orthonormal* set
which are linear
combinations of the original set and which span the same space.

*Proof: *We prove the theorem by constructing the desired orthonormal
set
sequentially from the original set .
This procedure is known as *Gram-Schmidt orthogonalization*.

First, note that for all , since is linearly dependent on every vector. Therefore, .

- Set .
- Define
as minus the projection of
onto
:
- Set
(
*i.e.*, normalize the result of the preceding step). - Define
as minus the projection of
onto
and
:
- Normalize: .
- Continue this process until
has been defined.

The Gram-Schmidt orthogonalization procedure will construct an
orthonormal basis from any set of linearly independent vectors.
Obviously, by skipping the normalization step, we could also form
simply an orthogonal basis. The key ingredient of this procedure is
that each new basis vector is obtained by subtracting out the
projection of the next linearly independent vector onto the vectors
accepted so far into the set. We may say that each new linearly
independent vector is projected onto the *subspace*
spanned by the vectors
, and any nonzero
projection in that subspace is subtracted out of to make the
new vector orthogonal to the entire subspace. In other words, we
retain only that portion of each new vector which ``points
along'' a new dimension. The first direction is arbitrary and is
determined by whatever vector we choose first ( here). The
next vector is forced to be orthogonal to the first. The second is
forced to be orthogonal to the first two (and thus to the 2D subspace
spanned by them), and so on.

This chapter can be considered an introduction to some
important concepts of *linear algebra*. The student is invited to
pursue further reading in any textbook on linear algebra, such as
[47].^{5.13}

Matlab/Octave examples related to this chapter appear in Appendix I.

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Nth Roots of Unity

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Signal/Vector Reconstruction from Projections