Some vector space proofs

In summary, we have shown that if u is orthogonal to a set of vectors in R^{n}, then it is also orthogonal to every vector in the span of that set. For the second question, we have not provided a complete proof, but a possible approach would be to use proof by contradiction and the fact that A is nonsingular to show that Av1, Av2, ..., Avn must be a basis for R^{n}.
  • #1
Bertrandkis
25
0
Question 1
Let u, v1,v2 ... vn be vectors in [tex]R^{n}[/tex]. Show that if u is orthogonal to v1,v2 ...vn then u is orthogonal to every vector in span{v1,v2...vn}
My attempt
if u is orthogonal to v1,v2 ...vn then[tex] (u.v1)+(u.v2)+...+(u.vn)=0[/tex]
Let w be a vector in span{v1,v2...vn} therefore
[tex] w=c1v1+c2v2+...+cnvn [/tex]
[tex] u.w=u(c1v1+c2v2+...+cnvn)[/tex]
=>[tex] c1(u.v1)+c2(u.v2)+...+cn(u.vn) =0 [/tex]
So u is orthogonal to w

Question 2
Let [tex] \{v1,v2...vn \}[/tex] be a basis for the n-dimensional vector space [tex]R^{n}[/tex].
Show that if A is a non singular matrix nxn then [tex] \{Av1,Av2...Avn \} [/tex] is also a basis for [tex]R^{n}[/tex].
Let w be a vector in [tex]R^{n}[/tex] therefore w can be written a linear combination of vectos in it's basis
[tex] x=c1v1+c2v2+...+cnvn [/tex]
[tex] Av1={\lambda}1x1[/tex],[tex] Av2={\lambda}2x2[/tex] ...[tex] Avn={\lambda}3xn[/tex]
so
[tex]Ax=A(c1v1+c2v2+...+cnvn) [/tex]
[tex]Ax={\lambda}1c1v1+{\lambda}2c2v2+...+{\lambda}ncnvn) [/tex]
therefore [tex] \{Av1,Av2...Avn \} [/tex] is also a basis for [tex]R^{n}[/tex].
 
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  • #2
Bertrandkis said:
Question 1
Let u, v1,v2 ... vn be vectors in [tex]R^{n}[/tex]. Show that if u is orthogonal to v1,v2 ...vn then u is orthogonal to every vector in span{v1,v2...vn}
My attempt
if u is orthogonal to v1,v2 ...vn then[tex] (u.v1)+(u.v2)+...+(u.vn)=0[/tex]
Let w be a vector in span{v1,v2...vn} therefore
[tex] w=c1v1+c2v2+...+cnvn [/tex]
[tex] u.w=u(c1v1+c2v2+...+cnvn)[/tex]
=>[tex] c1(u.v1)+c2(u.v2)+...+cn(u.vn) =0 [/tex]
So u is orthogonal to w
Yes, that's looks good. And you understand, I assume, that "u orthogonal to v1, v2 ..., vn" means u is orthogonal to each of v1, v2, ..., vn- that's where you get (u.v1)+ (u.v2)+ ...+ (u.vn)= 0+ 0+ ...+ 0= 0.

Question 2
Let [tex] \{v1,v2...vn \}[/tex] be a basis for the n-dimensional vector space [tex]R^{n}[/tex].
Show that if A is a non singular matrix nxn then [tex] \{Av1,Av2...Avn \} [/tex] is also a basis for [tex]R^{n}[/tex].
Let w be a vector in [tex]R^{n}[/tex] therefore w can be written a linear combination of vectos in it's basis
[tex] x=c1v1+c2v2+...+cnvn [/tex]
[tex] Av1={\lambda}1x1[/tex],[tex] Av2={\lambda}2x2[/tex] ...[tex] Avn={\lambda}3xn[/tex]
I don't understand this. Why is [tex] Av1={\lambda}1x1[/tex]? Are you assuming each of the basis vectors is an eigenvector of A? That is not given in the hypothesis.

so
[tex]Ax=A(c1v1+c2v2+...+cnvn) [/tex]
[tex]Ax={\lambda}1c1v1+{\lambda}2c2v2+...+{\lambda}ncnvn) [/tex]
therefore [tex] \{Av1,Av2...Avn \} [/tex] is also a basis for [tex]R^{n}[/tex].
Even if it were true that the original basis consists of eigenvectors of A, what you have done is show that Av1, Av2, ..., Avn span the space. You have not shown that they are independent. Also, you have not used the fact that A is nonsingular.

Better, I think, would be to use "proof by contradiction". Suppose Av1, Av2, ..., Avn were NOT independent. What would that tell you about v1, v2, ..., vn (remember that since A in nonsingular, it has an inverse matrix). Suppose Av1, Av2, ..., Avn does NOT span the space. That is, suppose there were some w such that a1Av1+ a2Av2+ ...+ anAvn was NOT equal to w for any choice of a1, a2, ..., an. What does that tell you about v1, v2, ..., vn and A-1w?
 

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of objects called vectors, which can be added together and multiplied by scalars (such as real numbers). The vectors must follow certain rules, such as closure under addition and scalar multiplication, to be considered a vector space.

2. What are the properties of a vector space?

The properties of a vector space include closure under addition and scalar multiplication, associativity, commutativity, existence of a zero vector, existence of additive inverses, and distributivity. These properties allow for the manipulation and calculation of vectors within a vector space.

3. How do you prove that a set is a vector space?

To prove that a set is a vector space, you must show that it satisfies all of the properties of a vector space. This can be done by showing that the set follows the rules of closure under addition and scalar multiplication, associativity, commutativity, existence of a zero vector, existence of additive inverses, and distributivity.

4. Can you give an example of a vector space proof?

One example of a vector space proof is proving that the set of real numbers, denoted as R, is a vector space. This can be done by showing that R follows all of the properties of a vector space, such as closure under addition and scalar multiplication, associativity, commutativity, existence of a zero vector, existence of additive inverses, and distributivity.

5. How are vector space proofs used in real life?

Vector space proofs are used in various fields of science and engineering, such as physics, computer science, and economics. In physics, vector space proofs are used to calculate and analyze forces and motions. In computer science, vector space proofs are used in algorithms and data structures. In economics, vector space proofs are used to model and analyze economic systems.

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