# The graduate demonstrates understanding of the properties

Question

LINEAR ALGEBRA

Competency 209.8.4: Vector Spaces – The graduate demonstrates understanding

of the properties and characteristics of vector spaces.

Introduction:

Vector spaces possess a collection of specific characteristics and properties. Use the

definitions in the attached “Definitions” to complete this task.

Define the elements belonging to R2 as {(a, b) | a, b ∈ R}. Combining elements

within this set under the operations of vector addition and scalar multiplication

should use the following notation:

Vector Addition Example: (–2, 10) + (–5, 0) = (–2 – 5, 10 + 0) = (–7, 10)

Scalar Multiplication Example: –10 × (1, –7) = (–10 × 1, –10 × –7) = (–10, 70), where

–10 is a scalar.

Under these definitions for the operations, it can be rigorously proven that R2 is a

vector space.

Given:

Laws 1 and 2 in the attached “Definitions” are true.

Requirements:

Provide a written explanation (suggested length of 2–4 pages) of why R2 is a vector

space in which you do the following:

A. Prove the truth of Laws 3 through 10 of the provided mathematical definition for a

vector space.

B. Give an example of a subset of R2 that is a nontrivial subspace of R2, showing all

work.

C. When you use sources, include all in-text citations and references in APA format.

Note: For definitions of terms commonly used in the rubric, see the Rubric Terms web

link included in the Evaluation Procedures section.

Note: When using sources to support ideas and elements in a paper or project, the

submission MUST include APA formatted in-text citations with a corresponding

reference list for any direct quotes or paraphrasing. It is not necessary to list sources

that were consulted if they have not been quoted or paraphrased in the text of the

paper or project.

Note: No more than a combined total of 30% of a submission can be directly quoted

or closely paraphrased from sources, even if cited correctly. For tips on using APA

style, please refer to the APA Handout web link included in the General Instructions

section.

Task 2 Definitions

The Definition of a Vector Space:

A vector space is a set V of elements (called vectors) together with two operations,

vector addition and scalar multiplication, satisfying the following 10 laws (for all

vectors X, Y, and Z in V and all (real) scalars r and s).

Laws for Addition

1. Closure under addition

If X and Y are any two vectors in V, then X + Y ∈ V.

2. Associative law

(X + Y) + Z = X + (Y + Z)

3. Commutative law

X+Y=Y+X

4. Additive identity law

There is a vector in V, denote 0 such that X + 0 = X where 0 is called the

zero vector.

5. Additive inverse law

For every X ∈ V there is a vector –X such that X + (–X) = 0, where –X is

called the additive inverse of X.

Laws for Scalar Multiplication

6. Closure under scalar multiplication

If X is any vector in V and r is any real scalar, then rX ∈ V.

7. Associative law

(rs)X = r(sX)

8. Distributivity over vector sums law

r(X + Y) = rX + rY

9. Distributivity over scalar sums law

(r + s)X = rX + sX

10. Scalar multiplication identity law

1X = X

The Definition of a Nontrivial Subspace of a Vector Space:

Any subspace W of V that is neither V nor {0} is a nontrivial subspace (note that the

set W needs to be proven a subspace before it can be proven to be a nontrivial

subspace).

7. Associative law

(rs)X = r(sX)

Let a , b ∈ R∧( x , y ) ∈V then, a ( b ( x , y ) )=a ( bx , by )=( abx , aby )=( ab )( x , y )

( rs ) X=( rs ) < x 1 , y 1 >¿

rs ¿ x 1 , ( rs ) x2 >¿

¿ <¿

¿<r ( s x 1 ) , r ( s x 2) > ¿

¿ r < s x1 , s x 2 >¿

s < x 1 , x 2> ¿

¿r ¿

¿ r (sX )

8. Distributivity over vector sums law

r(X + Y) = rX + rY

Distributivity of Scaler multiplication with respect ¿ vector addition :−¿

Let a ∈ R∧( x 1 , y 1 ) ∈V , ( x 2 , y 2 ) ∈V then,

a {( x 1 , y 1) + ( x2 , y 2 ) }=a ( x 1+ x 2 , y 1 + y 2 ) =( a { x 1 + x 2 }+ a { y 1+ y 2 } )

¿ ( a x 1+ a x2 , a y 1 +a y 2 )=( a x1 , a y 1 ) + ( a x 2 , a y 2 ) =a ( x 1 , y 1 ) +a ( x 2 , y 2 ) .

r {( x 1 , y 1 ) + ( x 2 , y2 ) }

= r ( x 1+ x 2 , y 1 + y 2 )

= ( r { x 1 + x 2} + r { y 1+ y 2 } )

= ( r x 1+r x 2 ,r y 1 +r y 2 )

= ( r x 1 , r y 1 )+ ( r x2 , r y 2)

= r ( x 1 , y 1 ) +r ( x 2 , y 2 )

=rX+rY

r(X + Y) =

9. Distributivity over scalar sums law

(r + s)X = rX + sX

Distributivity of Scaler multiplication with respect ¿ field addition :

Let a , b ∈ R∧( x , y ) ∈V then

( a+b )( x , y )=( ( a+ b ) x , ( a+ b ) y )=( ax +bx , ay+ by )=( ax , ay ) + ( bx ,by ) =a ( x , y ) +b ( x , y )

( r + s ) X= ( r+ s ) ( x1 , y 1)

¿ ( ( r + s ) x 1 , ( r + s ) x 2)

¿ ( r x 1 + s x 1 , r x 2+ s x 2 )

¿ ( r x 1 , r x2 ) + ( s x 1, s x 2 )

¿ r ( x 1 , x 2) + s ( x 1 , x 2 )

¿ rX + s X

10. Scalar multiplication identity law

1X = X

Scaler multiplication identity law :−¿

let 1 ∈ R∧( x , y ) ∈V then

1 ( x , y )=( 1 x , 1 y ) =( x , y ) .

Thus V satisfies all the condition of Vector Space HenceV is Vector Space .

1 X=1 ∙< x1 , x2 >¿

¿<1 x1 , 1 x 2 >¿

¿< x 1 , y 1 >¿

¿X

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