Chapter 4: Vector Spaces - Part 1/Slides By Pearson

4
4.1
© 2012 Pearson Education, Inc.
Vector Spaces
VECTOR SPACES AND
SUBSPACES

Topics
Vectors in the Plane and in 3D-Space

Vectors in the Plane and in 3-Space
Defn - A vector in the plane is a 2 x 1 matrix
where x and y are real numbers
Notation - In print, use bold letters for vectors.
When writing by hand use
Defn - The numbers x and y in the definition of a
vector are called the components of the vector v.
Defn - Two vectors and
are equal iff x1 = x2 and y1 = y2
x
y
v
v
1
1
x
y
v 2
2
x
y
u

A two-dimensional vector has several geometric
interpretations
1)A point ( x,y ) in the plane
2)A directed line segment from the origin to the point ( x,y )
3)A directed line segment from the point ( x1,y1 ) to the
point ( x2,y2 ). Then x = x2 x1, y = y2 y1
For applications to geometry and physics, some of the most
important operations are
1)Vector addition
2)Scalar multiplication
3)Vector subtraction
x
y
v

Vector Addition
Defn - Let and . Define
Geometric interpretation
1
2
u
u
u 1
2
v
v
v 1 1
2 2
u v
u v
u v

Vector Addition
u + v can be determined geometrically by adding
vectors head to tail
x
y
u
v
u v
v
If u and v have the same
length (to be defined later),
then u + v lies along the
bisector of the angle
between u and v

Scalar Multiplication
Defn - Let c be a scalar (e.g. a real number) and
be a vector. The scalar multiple c u of u by c is
defined as the vector
1
2
u
u
u
1
2
cu
cu

Vector Subtraction
Define u v as u + ( 1) v
Geometric relationship among u,v, u + v and u v

Parametric Description of a Line
Let P1 and P2 be points. The line passing through
them can be described as
1 2 1 1 21t t t tP P P P P P
P1
P2
P2 P1
t 0
t 1
t 1
t 0
0 t 1

Three-Dimensional Vectors
A three-dimensional vector is a 3 x 1 matrix
where x, y and z are real numbers
Vector addition, scalar multiplication and vector
subtraction are defined analogously to two
dimensions
x
y
z

Basic Properties of Vectors in R2 or R3
Theorem - If u, v and w are vectors in R2 or R3
and c and d are real scalars, then
a)u + v = v + u
b) u + (v + w) = (u + v) + w
c)u + 0 = 0 + u = u
d) u + ( u) = 0
e)c (u + v) = c u + c v
f)(c + d) u = c u + d u
g) c (d u) = (c d ) u
h) 1 u = u

1- 12
VECTOR SPACES AND SUBSPACES
Definition:
A vector space is a nonempty set V of objects,
called vectors,
on which are defined two operations,
called addition and multiplication by scalars
(real numbers),
subject to the ten axioms (or rules) listed below.
The axioms must hold for all vectors u, v, and w in V
and for all scalars c and d.

Vector Spaces
Axioms
1) If u, v Î∈ V, then
2) for all u, v, w Î∈ V
3) $∃ a unique 0 Î∈ V such that
4) "∀ u Î∈ V $∃ a unique v Î∈ V such that
5) "∀ u, v Î∈ V
6) "∀ u Î∈ V and "∀ real number c, Î∈ V
7) "∀ real numbers c and "∀ u, v Î∈ V,
8) "∀ real numbers c and d, "∀ u Î∈ V,
9) "∀ real numbers c and d, "∀ u Î∈ V,
10) "∀ u Î∈ V
u v V
u v w u v w
0 u u 0 u u V
u v v u 0
u v v u
c u
c u v c u c v
c d u c u d u
c d u cd u
1 u u

Vector Spaces
Comments
V is called a real vector space because scalars c
and d are real numbers, not because of any
characteristics of the elements of V, e.g. if the
elements of V involve complex numbers, but c and
d are real, then V is a real vector space
The identity element 0 for does not need to
have anything to do with the number zero
There is nothing in the definition that requires the
elements of V to be columns of numbers

Vector Spaces
Comments
To show that V is a vector space, we have to prove
properties 1 through 10. So, the verification
involves ten small proofs
To show that V is not a vector space, all that we
have to do is show that a single property does not
hold

1- 16© 2012 Pearson Education, Inc.
VECTOR SPACES AND SUBSPACES
For each u in V and scalar c,
0u 0
0 0c
u ( 1)u

SUBSPACES
Definition: A subspace of a vector space V is a
subset H of V that has three properties:
a. The zero vector of V is in H.
b. H is closed under vector addition. That is, for
each u and v in H, the sum is in H.
c. H is closed under multiplication by scalars.
That is, for each u in H and each scalar c, the
vector cu is in H.
u v

SUBSPACES
Properties (a), (b), and (c) guarantee that a subspace
H of V is itself a vector space, under the vector
space operations already defined in V.
Every subspace is a vector space.
Conversely, every vector space is a subspace (of
itself and possibly of other larger spaces).

Vector Spaces: Examples – 2D and 3D vectors
Example
The set of two-dimensional vectors, as defined
earlier, forms a vector space. The verification of
the properties follows from the properties of
matrices
Example
The set of three-dimensional vectors, as defined
earlier, forms a vector space. The verification of
the properties follows from the properties of
matrices

Vector Spaces: Examples – nD vectors & matrices
Example
The set Rn of all n x 1 matrices with real entries
forms a vector space with as matrix addition
and as scalar multiplication
Example
The set mRn of all real m x n matrices forms a
vector space with as matrix addition and as
scalar multiplication
1
2
n
x
x
x

Vector Spaces: Examples - Polynomials
Example
Consider polynomials in a variable t
where a0, a1, …, an are real numbers. If a0 ≠ 0, the
degree of p(t) is n. The zero polynomial
has no
degree. Polynomials of degree 0 are constants. Let
Pn be the set of all polynomials of degree ≤ n
together with the zero polynomial. Let p(t) be as
above and let
1
0 1 1
n n
nnp t a t a t a t a
1
0 0 0 0n n
t t t
1
0 1 1
n n
nnq t b t bt b t b

Vector Spaces : Examples - Polynomials
Example (continued)
Define as
For a real number c, define as
Pn forms a vector space
p t q t
1
0 0 1 1 1 1
n n
n nn np t q t a b t a b t a b t a b
c p t
1
0 1 1
n n
nnc p t ca t ca t ca t ca

Vector Spaces : Examples – Continuous fonctions
Example
Let V be the set of all real valued continuous
functions on the closed interval [ 0,1 ].
For f, g Î∈ V, define as
For f Î∈ V and a real number c, define as
V is a vector space.
It is commonly called C [ 0,1 ].
f g
0,1f g t f t g t t
c f
0,1c f t cf t t

Vector Spaces
Theorem - If V is a vector space, then
a) for every u in V
b) for every scalar c
c) If , then either c = 0 or u = 0
d) for every u in V
0 u 0
c 0 0
c u 0
1 u u

A SUBSPACE SPANNED BY A SET
The set consisting of only the zero vector in a vector
space V is a subspace of V, called the zero subspace
and written as {0}.
As the term linear combination refers to any sum of
scalar multiples of vectors, and Span {v1,…,vp}
denotes the set of all vectors that can be written as
linear combinations of v1,…,vp.

Example 2: Given v1 and v2 in a vector space V, let
. Show that H is a subspace of V.
Solution: The zero vector is in H, since .
To show that H is closed under vector addition, take
two arbitrary vectors in H, say,
and .
By Axioms 2, 3, and 8 for the vector space V,
1 2
Span{v ,v }H
1 2
0 0v 0v
1 1 2 2
u v vs s 1 1 2 2
w v vt t
1 1 2 2 1 1 2 2
1 1 1 2 2 2
u w ( v v ) ( v v )
( )v ( )v
s s t t
s t s t

So is in H.
Furthermore, if c is any scalar, then by Axioms 7 and 9,
which shows that cu is in H and H is closed under scalar
multiplication.
Thus H is a subspace of V.
u w
1 1 2 2 1 1 2 2
u ( v v ) ( )v ( )vc c s s cs cs

Theorem 1: If v1,…,vp are in a vector space V, then
Span {v1,…,vp} is a subspace of V.
We call Span {v1,…,vp} the subspace spanned (or
generated) by {v1,…,vp}.
Give any subspace H of V, a spanning (or
generating) set for H is a set {v1,…,vp} in H such
that
.1
Span{v ,...v }p
H

Chapter 4: Vector Spaces - Part 1/Slides By Pearson

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Chapter 4: Vector Spaces - Part 1/Slides By Pearson