**Section 3.1**

**Vector Spaces**

**Vector-Space Operations**

In Euclidean space we have,

1.) Addition of two vectors v, u in is again in . (Closed under vector addition.)

2.) Multiplication of a vector v in with a scalar is again in . (Closed under scalar multiplication.)

We have encountered subsets in that are closed under addition and multiplication. We called

these sets
__subspaces__**.**
(
Section 1.6)

`> `

`> `
**`S2 ={[x,y,z] | 2*x+3*y+z=0}`; **

`> `

Consider the set C[a,b] of continuous functions on an interval [a,b]. This set is closed

under addition and scalar multiplication. Can we make a general definition and not

restrict ourselves to Euclidean spaces .

**Definition of a Vector Space**

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A (real)
** vector space**
is a set V of objects called

adding any two vectors v and w to produce v+w in V and a rule for multiplying

any vector v in V by any scalar r in R to produce a vector rv in V. Moreover, there

must exist a vector 0 in V and for each v in V there must exist a vector -v in V such

that properties A1-A4 and S1-S4 hold for vectors v, w, u and scalars r,s,

A1: (u + v) + w = u + (v + w)

A2: v + w = w + v

A3: 0 + v = v

A4: v + (-v) = 0

S1: r(v + w) = rv + rw

S2: (r + s)v = rv + sv

S3: r(sv) = (rs)v

S4: 1v = v

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A "vector" in a general vector space does not always mean an element with a specified

number of components. It may refer to a function if we are dealing with a set of functions.

Many examples of such spaces are studied in precalculus, calculus,and differential equation

courses.

**Examples of Vector Spaces**

**Example 1:**
Set
of all n x 1 vectors is a vector space, using the usual

vector addition and scalar multiplication.

**Example 2: **
Set
of all m x n matrices is a vector space, using as vector

addition and scalar multiplication the usual addition of matrices and multiplication

of a matrix by a scalar.

**Example 3:**
Set
of all polynomials of degree <= n. i.e.

*p(x) =*
+ . . . +
with the usual addition and

scalar multiplication i.e.,

(p+q)(x) = p(x) + q(x) for p and q in

(rp)(x) = rp(x) for p in and r a scalar

is a vector space.

**Example 4: **
Set C[a,b] of all continous real-valued functions on [a,b] with

the usual addition and scalar multiplication i.e.,

(f+g)(x) = f(x) + g(x) for f and g in C[a,b]

(rf)(x) = rf(x) for f in C[a,b] and r a scalar

is a vector space.

**Example 5: **
Set S of all infinite sequences {
} of real numbers with scalar

multiplication r{ } = {r } and addition { } + { } = { } is

a vector space.

**More examples**

**Example 6: **
Let S be the set of real numbers with an unsual definition of addition,

(x + y) = max{x,y} and usual scalar multiplication (rx) = rx. Is S with these

operations a Vector Space?

Must show closure and properties A1-A4 and S1-S4.

A1: (u + v) + w = u + (v + w)

A2: v + w = w + v

A3: 0 + v = v

A4: v + (-v) = 0

S1: r(v + w) = rv + rw

S2: (r + s)v = rv + sv

S3: r(sv) = (rs)v

S4: 1v = v

**Example 7:**
let S be the set of all ordered pairs of real numbers with addition defined by

( ) + ( ) = ( ) and scalar multiplication r( ) = ( ).

Is S with these operations a Vector Space?

Must show closure and properties A1-A4 and S1-S4.

A1: (u + v) + w = u + (v + w)

A2: v + w = w + v

A3: 0 + v = v

A4: v + (-v) = 0

S1: r(v + w) = rv + rw

S2: (r + s)v = rv + sv

S3: r(sv) = (rs)v

S4: 1v = v

**Example 8: **
Let S denote the set of positive real numbers. Define the operation of scalar

multiplication as,

(rx) = and operation of addition as,

(x + y) = xy.

Is S a vector space with these operations?

Must show closure and properties A1-A4 and S1-S4.

A1: (u + v) + w = u + (v + w)

A2: v + w = w + v

A3: 0 + v = v

A4: v + (-v) = 0

S1: r(v + w) = rv + rw

S2: (r + s)v = rv + sv

S3: r(sv) = (rs)v

S4: 1v = v

**Properties of Vector Spaces**

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Theorem 3.1:

Every vector space V has the following properties:

1.) The vector 0 in V is unique.

2.) The vector -v in V is unique.

3.) If u + v = u + w then v = w.

4.) 0v = 0 for scalar 0 and vector v.

5.) r0 = 0 for scalar r and vector 0.

6.) (-r)v = r(-v) = -(rv) for scalar r and vector v.

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**Exercises**

1, 5, 9, 11, 13, 16, 18.