Linear Algebra: Kernel and Image question

In summary: I'm a little lost here! Thanks!I may be missing something, maybe even something obvious, but I think that given _any_ linear transformation L from R3 to R3, we can find a decomposition as in i), but it is not true for _every map_ L as above that kerL=KerL2. As example, take a linear map L , whose matrix representation M:=(mi,j) has all 0's, except for m1,3=m2,2=1. Then M2 is all 0's except for m2,2=1, and it is not true then that kerL=kerL2. Hope someone else can... Thanks!
  • #1
Tazz01
9
0

Homework Statement



T : R[itex]^{3}[/itex] -> R[itex]^{3}[/itex] is a linear transformation. We need to prove the equivalence of the three below statements.

i) R[itex]^{3}[/itex] = ker(T) [itex]\oplus[/itex] im(T);
ii) ker(T) = ker(T[itex]^{2}[/itex]);
iii) im(T) = im(T[itex]^{2}[/itex]).

Homework Equations



R[itex]^{3}[/itex] = ker(T) [itex]\oplus[/itex] im(T), if for all v [itex]\in[/itex] R[itex]^{3}[/itex] there exists x [itex]\in[/itex] ker(T) and y [itex]\in[/itex] im(T) such that v = x + y, and ker(T) [itex]\bigcap[/itex] im(T) = {0}

ker(T) = {x[itex]\in[/itex]R[itex]^{3}[/itex] : T(x)=0}

im(T) = { w[itex]\in[/itex]R[itex]^{3}[/itex] : w=f(x), x[itex]\in[/itex]R[itex]^{3}[/itex]}

The Attempt at a Solution



I really have no idea how to show these statements are equivalent. Can someone also clarify the linear mapping T[itex]^{2}[/itex]?

Thanks.
 
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  • #2
Tazz01 said:

Homework Statement



T : R[itex]^{3}[/itex] -> R[itex]^{3}[/itex] is a linear transformation. We need to prove the equivalence of the three below statements.

i) R[itex]^{3}[/itex] = ker(T) [itex]\oplus[/itex] im(T);
ii) ker(T) = ker(T[itex]^{2}[/itex]);
iii) im(T) = im(T[itex]^{2}[/itex]).

Homework Equations



R[itex]^{3}[/itex] = ker(T) [itex]\oplus[/itex] im(T), if for all v [itex]\in[/itex] R[itex]^{3}[/itex] there exists x [itex]\in[/itex] ker(T) and y [itex]\in[/itex] im(T) such that v = x + y, and ker(T) [itex]\bigcap[/itex] im(T) = {0}

ker(T) = {x[itex]\in[/itex]R[itex]^{3}[/itex] : T(x)=0}

im(T) = { w[itex]\in[/itex]R[itex]^{3}[/itex] : w=f(x), x[itex]\in[/itex]R[itex]^{3}[/itex]}

The Attempt at a Solution



I really have no idea how to show these statements are equivalent. Can someone also clarify the linear mapping T[itex]^{2}[/itex]?

Thanks.

For starters, T(T(x)) = T2(x).
 
  • #3
Are the domain and the codomain for T[itex]^{2}[/itex] the same as they were for T?

e.g. T[itex]^{2}[/itex]:R[itex]^{3}[/itex]->R[itex]^{3}[/itex]

This would mean that:

ker(T[itex]^{2}[/itex])={x[itex]\in[/itex]R[itex]^{3}[/itex] : T[itex]^{2}[/itex](x)=0}

Similarly for the image. It seems that to prove (ii) and (iii) are the same, we can use the rank-nullity theorem, but no idea for i-ii and i-iii. Any more ideas?
 
  • #4
Tazz01 said:
Are the domain and the codomain for T[itex]^{2}[/itex] the same as they were for T?

e.g. T[itex]^{2}[/itex]:R[itex]^{3}[/itex]->R[itex]^{3}[/itex]
Yes. Also, if x is in ker(T), then T(x) = 0. It's pretty easy to show that T(T(x)) = 0 as well, which says that if x is in ker(T), then x is in ker(T2).
Tazz01 said:
This would mean that:

ker(T[itex]^{2}[/itex])={x[itex]\in[/itex]R[itex]^{3}[/itex] : T[itex]^{2}[/itex](x)=0}

Similarly for the image. It seems that to prove (ii) and (iii) are the same, we can use the rank-nullity theorem, but no idea for i-ii and i-iii. Any more ideas?

Show that i ==> ii, then that ii ==> iii, and then finally, that iii ==> i. That's all you need to do to show that the three statements are equivalent.
 
  • #5
If x is in ker(T), this means that T(x)=0.

If we put this same x into T(T(x)), we get T(0) - but we don't know what T(0) equals...

I'm still lost on this, how can we approach the first part, showing that i ==> ii?
 
  • #6
If T is a linear transformation, T(a+b)=T(a)+T(b), and 0=0+0...

Still, unless I am missing something in your notation or otherwise, I think there are counterexamples:

For any T:R3→ R3, we have the decomposition in i) by,

say, choosing a basis for R3, and representing T using a matrix,

so that R3 is the direct sum of subspaces of complementary ( to 3)

dimension , by, as you said, rank nullity. Since rank, nullity intersect only in {0},

the two are subspaces of complementary dimension , so they vector-add to 3.

But this decomposition is true for _any_ linear map from R3 to

R3, but it is not always true that kerT2=kerT, nor that

ImT2=ImT (altho these last two are equivalent to each other for any

map T, by rank-nullity.). Am I missing something here?
 
Last edited:
  • #7
I think I figured out how to show ker(T)=ker(T[itex]^{2}[/itex]). Say for x [itex]\in[/itex] ker(T), then we have for T(T(x)):

T(T(x)) = T(0) <--- Now this is the zero vector inside the brackets

Can we pull this out as a contant, and use any vector v [itex]\in[/itex] R[itex]^{3}[/itex]:

T(0) = T(0v) = 0T(v) = 0

Therefore, we have shown that if x is in ker(T), it is also in ker(T[itex]^{2}[/itex]). Can someone confirm this?

Also, from the question, it would seem that ker(T) always equals ker(T[itex]^{2}[/itex]).

Bacle, what do you mean by counterexamples? I'm a little confused as to what you've said, are you able to clarify?
 
  • #8
Tazz01:

I may be missing something, maybe even something obvious, but I think that given _any_ linear transformation L from R3 to R3, we can find a decomposition as in i), but it is not true for _every map_ L as above that kerL=KerL2. As example, take a linear map L , whose matrix representation M:=(mi,j) has
all 0's, except for m1,3=m2,2=1. Then M2 is all 0's except for m2,2=1, and it is not true then that kerL=kerL2.

Hope someone else can double-check.
 
  • #9
Tazz01 said:
I think I figured out how to show ker(T)=ker(T[itex]^{2}[/itex]). Say for x [itex]\in[/itex] ker(T), then we have for T(T(x)):

T(T(x)) = T(0) <--- Now this is the zero vector inside the brackets

Can we pull this out as a contant, and use any vector v [itex]\in[/itex] R[itex]^{3}[/itex]:

T(0) = T(0v) = 0T(v) = 0
Yes, part of the definition of "linear transformation" is that T(av)= aT(v) for a any scalar so T(0)= T(0v)= 0T(v)= 0. Another part of the definition is that T(u+ v)= T(u)+ T(v) for any two vectors u and v. Here, T(u)= T(u+ 0)= T(u)+ T(0) so that T(0)= 0 from that.
You should, at least in your mind, distinguish between the 0 vector and the number 0 but it is always true that [itex]0\vec{v}= \vec{0}[/itex].

Therefore, we have shown that if x is in ker(T), it is also in ker(T[itex]^{2}[/itex]). Can someone confirm this?

Also, from the question, it would seem that ker(T) always equals ker(T[itex]^{2}[/itex]).

Bacle, what do you mean by counterexamples? I'm a little confused as to what you've said, are you able to clarify?
 
  • #10
So, to be more specific: Let L be the linear map L:R3→R3represented by M, with :

M=[ 0 1 0]
[ 0 0 1]
[ 0 0 0]

Then the kernel of M is the subspace spanned by {(x,0,0)}, i.e., if we have the standard xyz-axes, then the entire line is crushed to 0.

Now, M2=

[ 0 0 1]
[ 0 0 0]
[ 0 0 0]

Has as kernel the subspace {(0,x,y)} , so that kerM2± KerM
 
  • #11
I'm not sure about that Bacle, I will assume that the question has a solution and that ker(T) does equal ker(T^2).

Would anyone be able to advise how I could go about implying (ii) from (i)? Do I have to show that (i) holds for ker(T^2)? And then use rank-nullity from ii - iii?
 
  • #12
For this problem, given that R3 = ker(T) [itex]\oplus[/itex] im(T), apparently ker(T) = ker(T2). As I said in post #4, if x [itex]\in[/itex] ker(T), then x [itex]\in[/itex] ker(T2), but the converse is not necessarily true, as Bacle's counterexample shows.

From i) you know that dim(ker(T)) can be one of only four values: 0, 1, 2, or 3. So if dim(ker(T)) = n, then dim(Im(T)) = 3 - n. I suspect that you need to use this fact.
 
  • #13
I believe you can use the fact that [itex]Ker(T)\cap Im(T)=\left\{0\right\}[/itex] by the definition of

[itex]R^3=Im(T)\oplus Ker(T)[/itex]​

to easily prove that [itex]Ker(T)=Ker(T^2)[/itex].
 
  • #14
I've proved i-ii and ii-iii but not yet iii-i. This is something I'm stuck on, can someone advise?
 
  • #15
Try analyzing [itex]x-T(x)[/itex] and use the fact that [itex]im(T)\subseteq im(T^2)[/itex]
 
  • #16
Edit, I've now completed this question, thanks for your help guys.
 
Last edited:
  • #17
it looks false to me. i.e. i) looks always true but not either ii) or iii). maybe i) was stated wrong and should have said ker(T) (+) im(T^2)?

(always assuming "=" means "isomorphic".)

To construct counterexamples use the shift operator T taking (x,y,z) to (0,x,y). notice kerT^2 is larger than kerT. and hence image T^2 is smaller than image T.
 
  • #18
But then, T will violate all three conditions.

Notice that

im(T)={(0,y,z)}; im(T^2)={(0,0,z)}

ker(T)={(0,0,z)}; ker(T^2)={(0,y,z)}

Then,

i) im(T) (+) ker(T)={(0,y,z)} is pure subset of R^3
ii) im(T^2) is pure subset of im(T)
iii) ker(T) is pure subset of ker(T^2)
 
  • #19
Bacle said:
So, to be more specific: Let L be the linear map L:R3→R3represented by M, with :

M=

[ 0 1 0]
[ 0 0 1]
[ 0 0 0]

Then the kernel of M is the subspace spanned by {(x,0,0)}, i.e., if we have the standard xyz-axes, then the entire line is crushed to 0.

Now, M2=

[ 0 0 1]
[ 0 0 0]
[ 0 0 0]

Has as kernel the subspace {(0,x,y)} , so that kerM2± KerM

i'd like to point out that this is also not a counter-example:

im(M) = {(y,z,0) : y,z in R}

whereas ker(M) = {(x,0,0) : x in R},

so R3 ≠ ker(M)⊕im(M).

so the conditions don't hold for "any" linear transformations on R3, just certain ones.
 
  • #20
How to prove im(T)⊆im(T 2 )?
 

1. What is a kernel in linear algebra?

A kernel, also known as the null space, is the set of all vectors that when multiplied by a matrix result in a zero vector. In other words, it is the set of all solutions to the equation Ax = 0, where A is a matrix and x is a vector.

2. What is the importance of the kernel in linear algebra?

The kernel is important because it helps us understand the relationship between the inputs and outputs of a linear transformation. It also allows us to find solutions to systems of linear equations and determine if a system is consistent or inconsistent.

3. How is the kernel related to the image in linear algebra?

The kernel and image are two fundamental subspaces of a matrix. The kernel is the set of all vectors that are mapped to the zero vector by the matrix, while the image is the set of all possible outputs of the matrix. They are related by the nullity-rank theorem, which states that the dimension of the kernel plus the dimension of the image equals the number of columns in the matrix.

4. How can we determine the dimension of the kernel and image?

The dimension of the kernel can be found by counting the number of free variables in the reduced row-echelon form of the matrix. The dimension of the image can be found by counting the number of pivot columns in the matrix.

5. What is the difference between a one-to-one and onto linear transformation?

A one-to-one linear transformation, also known as an injection, maps each input vector to a unique output vector. This means that the dimension of the kernel is zero. An onto linear transformation, also known as a surjection, maps the entire input space to the entire output space. This means that the dimension of the image is equal to the number of output dimensions.

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