Linear Algebra - vector spaces

In summary: The kernel is (t,t,t) so its basis is {(1,0,0),(0,1,0),(0,0,1)}Is that not 3 dimensions?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)}The rank of a linear transformation is 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. The dimension of
  • #1
underacheiver
12
0

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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  • #2
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?
 
  • #3
updated above
 
  • #4
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?
 
  • #5
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.
 
  • #6
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?
 
  • #7
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?
 
  • #8
(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 teh 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)
 
  • #9
ah~ ok, i see. thank you.
 

1. What is a vector space?

A vector space is a mathematical structure consisting of a set of vectors and a set of operations that can be performed on those vectors. These operations include addition, multiplication by a scalar, and a zero vector. The vectors in a vector space can represent physical quantities such as forces, velocities, and displacements.

2. What are the properties of a vector space?

A vector space must satisfy several properties, including closure under vector addition and scalar multiplication, associativity and commutativity of vector addition, existence of a zero vector, existence of additive inverse, and distributivity of scalar multiplication over vector addition. These properties ensure that the set of vectors in a vector space can be manipulated mathematically in a consistent and meaningful way.

3. How is a vector space different from a set of vectors?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations, while a set of vectors is simply a collection of vectors. A vector space has specific properties and rules that govern how the vectors can be manipulated, whereas a set of vectors does not necessarily have these properties.

4. What is the dimension of a vector space?

The dimension of a vector space is the number of vectors in a basis for that space. A basis is a set of linearly independent vectors that can be used to represent any vector in the space. The dimension of a vector space is often denoted by the symbol "n" and is used to describe the size or number of degrees of freedom of the space.

5. How is linear independence related to vector spaces?

Linear independence is a property of a set of vectors in a vector space. It means that none of the vectors in the set can be written as a linear combination of the other vectors. In other words, no vector in the set is redundant or can be expressed in terms of the other vectors. This property is important in determining the dimension of a vector space and in solving systems of linear equations.

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