Vector spaces are the principal object of study
in linear algebra. A vector space is always defined with respectto a field of scalars.
Fields
A field is a set
equipped with two operations, addition and
mulitplication, and containing two special members 0 and 1(
), such that for all
there exists
such that
there exists
such that
More concisely
is an
abelian group under addition
is an
abelian group under multiplication
multiplication distributes over addition
Examples
,,
Vector spaces
Let
be
a field, and
a
set. We say
is a vector space over
if there exist two operations, defined for all
,
and
:
vector addition: (
,
)
scalar multiplication:
(
,
)
and if there exists an element denoted
, such that the following hold for all
,
, and
,
, and
there exists
such that
More concisely,
is an abelian
group under plus
Natural properties of scalar multiplication
Examples
is a vector space over
is a vector space over
is a vector space over
is
not a vector space
over
The elements of
are called
vectors .
Euclidean space
Throughout this course we will think of a signal
as a vector
The samples
could be samples from a finite duration, continuous
time signal, for example.
A signal will belong to one of two vector spaces:
Real euclidean space
(over)
Complex euclidean space
(over)
Subspaces
Let
be a vector
space over
.
A subset
is called a
subspace of
if
is a vector space over
in its own right.
,
,
.
Are there other subspaces?
is a subspace if and only if for all
and
and for all
and
,
Linear independence
Let
.
We say that these vectors are
linearly
dependent if there exist scalars
such that
and at least one
.
If
only holds for the case
, we say that the vectors are
linearly
independent .
so these vectors are linearly dependent in
.
Spanning sets
Consider the subset
. Define the
span of
Fact:
is a subspace of
.
,
,
,
,
.
Aside
If
is infinite, the notions of
linear independence and span are easily generalized:
We say
is linearly independent if, for
every finite collection
, (
arbitrary) we
have
The span of
is
In both definitions, we only consider
finite sums.
Bases
A set
is called a
basis for
over
if and only if
is linearly independent
Bases are of fundamental importance in signal processing. They
allow us to decompose a signal into building blocks (basisvectors) that are often more easily understood.
= (real or complex) Euclidean
space,
or
.
where the 1 is in the
position.
over.
which is the DFT basis.
where
.
Key fact
If
is a basis for
,
then every
can be written uniquely (up to order of terms) in
the form
where
and
.
Other facts
If
is a
linearly independent set, then
can be extended to a basis.
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