Linear Algebra/ Linearly Independent

In summary: OK, let's put it this way: start with the original equation, and apply T to it. Then use the fact that T^m(x)=0. That should give you what you need.OK, If you apply T to (c0)x + (c1)T(x) + ...(cm-1)T^m-1(x)=0 you would get (c0)T(x) + (c1)T^2(x) + ...+ (cm-2)T^m-1(x) +(cm-1)T^m(x)=T(0)=0 then I'm stuck again. Can you help me from here?In
  • #1
Wildcat
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Homework Statement



Let T be a linear transformation of a vector space V into itself. Suppose x ε V is such that Tm(x)=0, Tm-1(x) not equal 0 for some positive integer m. show that x, T(x), …, Tm-1(x) are linearly independent.

In regards to Tm and Tm-1 m and m-1 are upperscript on the T. I don't know how to make it do that on this site.




The Attempt at a Solution



To show linearly independent, show that the only linear combination of transformations that = 0 is the one in which the coefficients are zero. For every xεV and cεR, real numbers, Suppose that c1, c2, ...,cm are scalars s.t. (c1)x + (c2)T(x) + ...(cm)Tm-1(x)=0 then
Σci Tm-1(x) =0 i=1 to m which equals Σci ΣTm-1(x) =0 Therefore all c's =0 hence linearly independent. Tm(x) could not be included since it equals 0 which means c could be nonzero.

This doesn't seem right (too easy)?
 
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  • #2
Wildcat said:

Homework Statement



Let T be a linear transformation of a vector space V into itself. Suppose x ε V is such that Tm(x)=0, Tm-1(x) not equal 0 for some positive integer m. show that x, T(x), …, Tm-1(x) are linearly independent.

In regards to Tm and Tm-1 m and m-1 are upperscript on the T. I don't know how to make it do that on this site.

The Attempt at a Solution



To show linearly independent, show that the only linear combination of transformations that = 0 is the one in which the coefficients are zero. For every xεV and cεR, real numbers, Suppose that c1, c2, ...,cm are scalars s.t. (c1)x + (c2)T(x) + ...(cm)Tm-1(x)=0 then
Σci Tm-1(x) =0 i=1 to m which equals Σci ΣTm-1(x) =0 Therefore all c's =0 hence linearly independent. Tm(x) could not be included since it equals 0 which means c could be nonzero.

This doesn't seem right (too easy)?

I don't see anything in there that looks even vaguely correct. Take the case m=2. Then you know T^2(x)=0 and you want to look at c1*x+c2*T(x)=0. Suppose you operate on that equation with T?
 
  • #3
True, obviously I need to think this through a little more.
 
  • #4
What if I get rid of the Σ symbols and say c1*x + c2*T(x) + ... cm* T^m-l(x) but before I go further, since you said you didn't see anything vaguely correct, I'm afraid I'm not even approaching this from the right direction?
 
  • #5
Wildcat said:
What if I get rid of the Σ symbols and say c1*x + c2*T(x) + ... cm* T^m-l(x) but before I go further, since you said you didn't see anything vaguely correct, I'm afraid I'm not even approaching this from the right direction?

Sure, it's probably easier to read c1*x+...+cm*T^(m-1)(x) than the sigma notation. And no, I don't think your direction is correct. Like I said in my last post. Try thinking about the case m=2 first. It should make things a lot clearer.
 
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  • #6
ok so nothing seems clear to me :( I did read in a definition that any subset of a vector space that contains the zero vector is linearly dependent. So that would be the reason the T^m(x) could not be included.

for the case m=2 where c1*x + c2*T(x) = 0 I'm afraid to ask this but here goes,

what if you choose x=1 and then T(1) could be -1 (since T^1 not equal 0 it could be negative?) then c1 +(-c2) =0 then c1=c2 which is not the trivial solution 0.

From my original question, I know this is wrong, but why?
 
  • #7
Wildcat said:
ok so nothing seems clear to me :( I did read in a definition that any subset of a vector space that contains the zero vector is linearly dependent. So that would be the reason the T^m(x) could not be included.

for the case m=2 where c1*x + c2*T(x) = 0 I'm afraid to ask this but here goes,

what if you choose x=1 and then T(1) could be -1 (since T^1 not equal 0 it could be negative?) then c1 +(-c2) =0 then c1=c2 which is not the trivial solution 0.

From my original question, I know this is wrong, but why?

It's wrong because x and T(x) are vectors. Not numbers. If they were just numbers, then you would be right and they couldn't possibly be independent.
 
  • #8
OK (this will be a good test in patience for you)

suppose x=(1,1) and T(x)=(-1,-1)
 
  • #9
Wildcat said:
OK (this will be a good test in patience for you)

suppose x=(1,1) and T(x)=(-1,-1)

They aren't linearly independent, are they?
 
  • #10
Wildcat said:
OK (this will be a good test in patience for you)

suppose x=(1,1) and T(x)=(-1,-1)

You are asking the right kind of questions, because they are showing you really don't understand what linear independence is. And that's a good thing, believe it or not. You have a good example of two vectors that are NOT linearly independent. Now try and show that (1,1) and (-1,1) ARE linearly independent. This is a ways from your original question, but once you figure out what linear independence actually means, it will be a lot easier.
 
  • #11
Thank you for being patient with me. In your first reply you mentioned to operate on that equation with T, which is what my professor suggested to our class. So, I am going to spend a little time in thought on that and try it again.

I think my confusion with linear independence is when it's with transformations? Hopefully a little more study will help.
Thanks again!
 
  • #12
OK, If you apply T to (c0)x + (c1)T(x) + ...(cm-1)T^m-1(x)=0 you would get
(c0)T(x) + (c1)T^2(x) + ...+ (cm-2)T^m-1(x) +(cm-1)T^m(x)=T(0)=0 then I'm stuck again. Can you help me from here?
 
  • #13
Wildcat said:
OK, If you apply T to (c0)x + (c1)T(x) + ...(cm-1)T^m-1(x)=0 you would get
(c0)T(x) + (c1)T^2(x) + ...+ (cm-2)T^m-1(x) +(cm-1)T^m(x)=T(0)=0 then I'm stuck again. Can you help me from here?

In this case you are better off applying T^(m-1) to that expression.
 
  • #14
ok then (c0)T^m-1(x) +(c1)T^m(x) +...+ (ck)T^m-1+k +... + (cm-1)T^m-1+m-1 =0

so (c0)T^m-1(x)=0 and c0 = 0 by Induction Principle (multiplying by T^m-k-1) all ck =0 and the set is linearly independent.


My classmate found this proof in a book. He didn't understand it and I don't either.

what happened to the (c1)T^m(x) the c1 wouldn't have to be 0?
 
  • #15
Wildcat said:
ok then (c0)T^m-1(x) +(c1)T^m(x) +...+ (ck)T^m-1+k +... + (cm-1)T^m-1+m-1 =0

so (c0)T^m-1(x)=0 and c0 = 0 by Induction Principle (multiplying by T^m-k-1) all ck =0 and the set is linearly independent.


My classmate found this proof in a book. He didn't understand it and I don't either.

what happened to the (c1)T^m(x) the c1 wouldn't have to be 0?

When you apply T^(m-1) you get c0*T^(m-1)(x)=0 since all of the higher powers of T applied to x are zero. Since T^(m-1)(x) is nonzero, that means c0=0. So substitute c0=0 and then apply T^(m-2). What do you conclude about c1?
 
  • #16
apply T^(m-2) to (c1)T^m(x)+... ??
 
  • #17
Wildcat said:
apply T^(m-2) to (c1)T^m(x)+... ??

No. Apply it to (c1)T(x)+...=0. The original expression with c0 set to 0.
 
  • #18
apply T^(m-2) to (c1)T^m(x)+... ??
 
  • #19
Wildcat said:
apply T^(m-2) to (c1)T^m(x)+... ??

No. Apply it to (c1)T(x)+...=0. The original expression with c0 set to 0.
 
  • #20
then c1=0?? since applying T^(m-2) to (c1)T^(x) gives you (c1)T^(m-1)x

I'm not sure how to ask this or if you can even explain in writing but why do we keep applying T?
 
  • #21
Wildcat said:
then c1=0?? since applying T^(m-2) to (c1)T^(x) gives you (c1)T^(m-1)x

I'm not sure how to ask this or if you can even explain in writing but why do we keep applying T?

You start with:
(c0)x+(c1)T(x) + (c2)T^2(x) + ...+ (cm-1)T^m-1(x)=0
You want to show those vectors are linearly independent. That means you want to show all of the c's must be 0. If you operate with T^(m-1) that gets rid of everything except for c0*T^(m-1)(x)=0. So you conclude c0=0. Put that into the first expression. So now you have:
(c1)T(x) + (c2)T^2(x) + ...+ (cm-1)T^m-1(x)=0
Operate with T^(m-2). That kills everything except for c1*T^(m-1)=0. So you conclude c1=0. Etc Etc.
 
  • #22
OK, I think my light bulb came on! You are an excellent teacher! You deserve another award under your name.

Thank you so much for all of your patience!
 

FAQ: Linear Algebra/ Linearly Independent

What is linear algebra?

Linear algebra is a branch of mathematics that deals with linear equations and their representations in vector spaces. It involves the study of linear transformations and their properties, as well as the manipulation of matrices and vectors to solve equations and represent data.

What does it mean for a set of vectors to be linearly independent?

A set of vectors is said to be linearly independent if none of the vectors in the set can be written as a linear combination of the other vectors. This means that no vector in the set is redundant and they all contribute unique information to the overall representation.

How do you determine if a set of vectors is linearly independent?

To determine if a set of vectors is linearly independent, you can use the definition above and try to represent one vector as a linear combination of the others. If this is possible, then the set is linearly dependent. Another method is to use the determinant of the matrix formed by the vectors. If the determinant is zero, then the set is linearly dependent.

Why is linear independence important in linear algebra?

Linear independence is important because it allows us to easily represent and manipulate data using vectors and matrices. It also helps us to understand the relationships between different vectors and how they can be combined to form new vectors. In addition, linearly independent sets of vectors are the basis for many important concepts and applications in linear algebra, such as eigenvalues and eigenvectors.

How is linear independence used in real-world applications?

Linear independence is used in a variety of real-world applications, such as computer graphics, data analysis, and machine learning. In computer graphics, linearly independent vectors are used to represent geometric transformations and create 3D models. In data analysis, linear independence is used to identify important features and reduce the dimensionality of large datasets. In machine learning, linearly independent vectors are used to represent data and train models to make predictions and classifications.

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