Inner Product Spaces
We have seen that linear vector spaces provide a useful
framework for describing a broad range of classes of signals,
both discrete-time and continuous-time. The notions of vector
length and distances between vectors were introduced with the
concept of normed linear vector spaces
(i.e., linear vector spaces with a norm).
However, optimization and signal approximation are still
problematic in many normed linear spaces, as, in general, the
solution may not be unique and it may be difficult to find the
solution(s).
Consider the vector space
ℝ
3
ℝ
3
with the Euclidean norm
∥x∥=
x
1
2
+
x
2
2
+
x
3
2
12
x
x
1
2
x
2
2
x
3
2
1
2
. This
three-dimensional normed linear space, unlike many of the normed linear
spaces we have seen, has many of the properties that we expect from
everyday 3-D life:
- The unit sphere (i.e., all vectors
xx such that
∥x∥=1
x
1
) is round.
- Any subspace (i.e., plane or line)
has a unique closest point to any element ss in
ℝ
3
ℝ
3
.
Practical experience indicates that the line from
ss to the
best approximation
s
̂
s
̂
in the subspace
Φ
2
Φ
2
is
perpendicular to the plane
Φ
2
Φ
2
. Normed linear spaces do not have the necessary
structure to handle the concepts of orthogonality or angles
between vectors. However,
ℝ
3
ℝ
3
with the Euclidean norm turns out to be one example
of an inner product space, and it is possible to define
orthogonality in inner product spaces.
- Definition 1:
Inner Product
In a complex linear space
SS an inner product
<x,y>
x
y
assigns a complex number to any pair of elements
xy∈S
x
y
S
.
The inner product must satisfy the following three conditions:
-
<x,y>=<y,x>¯
x
y
y
x
-
For any complex scalars aa,
bb,
<ax+by,z>=a<x,z>+b<y,z>
a
x
b
y
z
a
x
z
b
y
z
-
<x,x>≤0
x
x
0
and
<x,x>=0⇔x=0
⇔
x
x
0
x
0
.
In
ℝ
N
ℝ
N
, the usual dot product
<x,y>=∑k=0N
x
k
y
k
¯=
y
′x
x
y
k
0
N
x
k
y
k
y
x
is one possible inner product.
For complex-valued continuous-time signals in
C
ab
C
a
b
<x,y>=∫abxtyt¯dt
x
y
t
a
b
x
t
y
t
is the most common inner product.