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Vector Spaces

Module by: Richard Baraniuk. E-mail the author

Recall that we know how to add two signal/vectors and scale a vector/signal.

A vector space VV is a non empty subset of vectors such that given xx, yV y V , then (x+y)V x y V , and given xV x V , and a scalar αα, then αxV α x V . In other words, a vector space is closed under addition and scaling.


Depending on whether αR α or C we can distinguish real vectors spaces from complex vector spaces.

Example 1: Vector Spaces

R, R2 2 , R3 3 , ...... RN N

C, C2 2 , C3 3 , ...... CN N

a line in R2 2 through the origin

a line in R3 3 through the origin

a plane in R3 3 through the origin


0 0

Exercise 1

Vector spaces?

Figure 1
Figure 1 (spaces1.png)

A subspace of a vector space is a subset of the vectors that is itself a vector space.

Example 2

Vectors lying in a line through the origin in R2 2 :

Figure 2
Figure 2 (spaces2.png)

Exercise 2

Fundamental idea in DSP: split the vector space of all signals into "signal space" and "noise space". Why is this reasonable?

Show that the set of all linear combinations of MM vectors in RN N is a vector space.

If a vector space consists of all linear combinations of MM vectors, then these vectors span the vector space. In other words, the spanning vectors can generate a vector in the vector space.

Example 3

The vector 11 1 1 spans this subspace in R2 2 :

Figure 3
Figure 3 (spaces3.png)

Example 4

The canonical δδ basis, δ 0 ... δ N1 δ 0 ... δ N 1 , span RN N .

Exercise 3

Can M<N M N vectors span RN N ?

Do MN M N vectors always span RN N ?

Exercise 4

Is RN N a subspace if CN N ?

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