Linear Algebra - vector spaces

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Homework Help Overview

The discussion revolves around a linear algebra problem involving a transformation defined by a matrix A and its properties, including the computation of T(x), the kernel, nullity, and rank of the transformation. The participants explore the implications of the rank-nullity theorem and the characteristics of vector spaces.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the computation of T(x) using matrix multiplication and the implications of the kernel as the solution to Ax=0. There are attempts to determine the nullity and rank based on the dimensions of the kernel and the transformation's image.

Discussion Status

Some participants express uncertainty about the nullity and rank, questioning their understanding of the kernel's dimension. Others provide insights into the nature of the kernel and its basis, leading to a deeper exploration of the concepts involved. Multiple interpretations of the kernel's dimension are being discussed, indicating an active engagement with the material.

Contextual Notes

There is a noted confusion regarding the basis of the kernel and its dimensionality, with some participants asserting differing views on what constitutes a basis for the kernel subspace. The discussion reflects the complexity of understanding linear transformations and their properties in the context of vector spaces.

underacheiver
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Homework Statement


part a.
Use the matrix A =
{[1,-1,0]
[0,-1,1]
[-1,2,-1]}
to compute T(x) for x =
{[1]
[2]
[3]}
Here, T:R^3-->R^3 is defined as T(x)=Ax.

part b.
describe the kernel of the transformation.

part c.
what is the nullity of the tarnsformation

part d.
what is the rank of the transformation

Homework Equations


I guess the only equation is the rank nullity theorum, and T(x)=Ax

The Attempt at a Solution


a.
i multiplied the matrix A and x and got
T(x)=
{[-1]
[1]
[0]}
this part wasn't too hard.

b.
the kernel is the solution of Ax=0, right?
i solved the matrix equation and got ker T=
{[t]
[t]
[t]}
Is this correct?

c. the nullity is the dimension of the kernel space, so is it 1?? (i am guessing)

d. the dimension of the space is 3, so the rank is 2?? according to the rank nullity theorum?? (once again, guessing)

I'm sure i am doing something wrong. what is the logic behind parts c and d?
 
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underacheiver said:

Homework Statement


part a.
Use the matrix A =
{[1,-1,0]
[0,-1,1]
[-1,2,-1]}
to compute T(x) for x =
{[1]
[2]
[3]}
Here, T:R^3-->R^3 is defined as T(x)=Ax.

part b.
describe the kernel of the transformation.

part c.
what is the nullity of the tarnsformation

part d.
what is the rank of the transformation

Homework Equations


I guess the only equation is the rank nullity theorum, and T(x)=Ax

The Attempt at a Solution


a.
i multiplied the matrix A and x and got
T(x)=
{[-1]
[1]
[0]}
this part wasn't too hard.

b.
the kernel is the solution of Ax=0, right?
i solved the matrix equation and got ker T=
{[t]
[t]
[t]}
Is this correct?
looks reasonable to me, this represents all vectors on the line t.(1,1,1) for any number t
underacheiver said:
c. the nullity is the dimension of the kernel space, so is it 1?? (i am guessing)
yes so you found the kernal space is line, which has dimension 1
underacheiver said:
d. the dimension of the space is 3, so the rank is 2?? according to the rank nullity theorum?? (once again, guessing)
so the rank is 2, for a linear transformation, the rank is equal to the dimension of the image of the transformation. IN this case the image is a plane, so every vector will be mapped to some vector within that plane

consider any vector, v. you could write it as v = k + p. where k is the component along the vector (1,1,1) and p is the rest, which will lie in the image plane

when you apply the transformation you get

T(v) = T(k+p) = A.(k+p) = A.k + A.p = 0 + A.p = A.p

and the result A.p will in the image plane

A good example to use is the projector into xy plane
[100]
[010]
[000]

the null space is any vector along the z axis, while the image is the xy plane, and in fact gives the projection of any vector onto that plane

note though that in general transformations (like yours) might rotate and stretch vectors within the image plane as well...

underacheiver said:
I'm sure i am doing something wrong. what is the logic behind parts c and d?
 
updated above
 
For part c, why is the nullity 1? The kernel is (t,t,t) so its basis is {(1,0,0),(0,1,0),(0,0,1)}
is that not 3 dimensions?
 
underacheiver said:
For part c, why is the nullity 1? The kernel is (t,t,t) so its basis is {(1,0,0),(0,1,0),(0,0,1)}
is that not 3 dimensions?
Your matrix A maps any vector that is a multiple of (1, 1, 1) to the zero vector in R3. Any vector that is not a multiple of (1, 1, 1) does not get mapped to 0. A basis for this subspace is {(1, 1, 1)}.

The set of all such vectors is a one-dimensional subspace of R3, so nullity(A) = 1. Here the kernel(A) is of dimension 1, and you have two more dimensions to account for in R3.
 
underacheiver said:
For part c, why is the nullity 1? The kernel is (t,t,t) so its basis is {(1,0,0),(0,1,0),(0,0,1)}
is that not 3 dimensions?

As Mark pointed out {(1,0,0),(0,1,0),(0,0,1)} is not a basis for t.(1,1,1,).

And none of the vectors{(1,0,0),(0,1,0),(0,0,1)}, are in the kernel. Only the linear sum of equal amounts of each gives t(1,1,1), the null space (kernel).

Try T((1,0,0)) what do you get?
 
but t(1,1,1) can be written as a linear combination of the set {(1,0,0),(0,1,0),(0,0,1)}.
am i getting something mixed up here?
 
(1,1,1) can be written as a linear combination of the basis you gave. This is because the basis you gave spans all of R^3, and (1,1,1) is in R^3

the difference is we want a basis for the subspace defined by the line through the origin t(1,1,1). This is the null space of your operator

A basis is a minimal number linearly independent vectors from within the space. (1,0,0) is not within the line, hence it is not mapped to zero by the operator

when we pick a vector within the line say (2,2,2), any other vector in the line can be written as a linear multiple of (2,2,2). so (2,2,2 is a basis for the line. so is (-1,-1,-1)
 
ah~ ok, i see. thank you.
 

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