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Vector spaces are the principal object of study in linear algebra. A vector space is always defined with respectto a field of scalars.


A field is a set F equipped with two operations, addition and mulitplication, and containing two special members 0 and 1( 0 1 ), such that for all a b c F

    • a b F
    • a b b a
    • ( a + b ) + c a + ( b + c )
    • a 0 a
    • there exists a such that a a 0
    • a b F
    • a b b a
    • a b c a b c
    • a 1 a
    • there exists a such that a a 1
  • a b c a b a c
More concisely
  • F is an abelian group under addition
  • F is an abelian group under multiplication
  • multiplication distributes over addition



Vector spaces

Let F be a field, and V a set. We say V is a vector space over F if there exist two operations, defined for all a F , u V and v V :

  • vector addition: ( u , v ) u v V
  • scalar multiplication: ( a , v ) a v V
and if there exists an element denoted 0 V , such that the following hold for all a F , b F , and u V , v V , and w V
    • u + ( v + w ) ( u + v ) + w
    • u v v u
    • u 0 u
    • there exists u such that u u 0
    • a u v a u a v
    • a b u a u b u
    • a b u a b u
    • 1 u u
More concisely,
  • V is an abelian group under plus
  • Natural properties of scalar multiplication


  • N is a vector space over
  • N is a vector space over
  • N is a vector space over
  • N is not a vector space over
The elements of V are called vectors .

Euclidean space

Throughout this course we will think of a signal as a vector x x 1 x 2 x N x 1 x 2 x N The samples x i 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

x N (over)

Complex euclidean space

x N (over)


Let V be a vector space over F .

A subset S V is called a subspace of V if S is a vector space over F in its own right.

V 2 , F , S any line though the origin .

S is any line through the origin.

Are there other subspaces?

S V is a subspace if and only if for all a F and b F and for all s S and t S , a s b t S

Linear independence

Let u 1 , , u k V .

We say that these vectors are linearly dependent if there exist scalars a 1 , , a k F such that

i 1 k a i u i 0
and at least one a i 0 .

If only holds for the case a 1 a k 0 , we say that the vectors are linearly independent .

1 1 -1 2 2 -2 3 0 1 -5 7 -2 0 so these vectors are linearly dependent in 3 .

Spanning sets

Consider the subset S v 1 v 2 v k . Define the span of S < S > span S i 1 k a i v i a i F

Fact: < S > is a subspace of V .

V 3 , F , S v 1 v 2 , v 1 1 0 0 , v 2 0 1 0 < S > xy-plane .

< S > is the xy-plane.


If S is infinite, the notions of linear independence and span are easily generalized:

We say S is linearly independent if, for every finite collection u 1 , , u k S , ( k arbitrary) we have i 1 k a i u i 0 i a i 0 The span of S is < S > i 1 k a i u i a i F u i S k

In both definitions, we only consider finite sums.


A set B V is called a basis for V over F if and only if

  • B is linearly independent
  • < B > V
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.

V = (real or complex) Euclidean space, N or N . B e 1 e N standard basis e i 0 1 0 where the 1 is in the i th position.

V N over. B u 1 u N which is the DFT basis. u k 1 2 k N 2 k N N 1 where -1 .

Key fact

If B is a basis for V , then every v V can be written uniquely (up to order of terms) in the form v i 1 N a i v i where a i F and v i B .

Other facts

  • If S is a linearly independent set, then S can be extended to a basis.
  • If < S > V , then S contains a basis.

Questions & Answers

Application of nanotechnology in medicine
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I think
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Brian Reply
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industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
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what is Nano technology ?
Bob Reply
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The nanotechnology is as new science, to scale nanometric
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Introduction about quantum dots in nanotechnology
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Source:  OpenStax, Statistical signal processing. OpenStax CNX. Jun 14, 2004 Download for free at http://cnx.org/content/col10232/1.1
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