How Do You Determine the Basis, Dimension, and Rank of Vector Sets?

In summary: I get what you are saying.(B) I think the dimension I was referring to is the dimension of the null space. . .I just didn't know that's what it was called. I guess what confused me was that the dimension spanned by the vectors is asking for something completely different. . .or maybe not. . .I guess I'm confused about that. I thought the dimension spanned by the vectors was asking "How many vectors do I need to span the space?" which has to do with the basis. . .but maybe it's not asking that? And yes I was talking about the matrix having the vectors as columns.(C) Yeah that was sloppy on my part,
  • #1
_Bd_
109
0

Homework Statement



for the set of vectors:
v_1 = 1, -2, 0, 0, 3
v_2 = 2, -5, -3, -2, 6
v_3 = 0, 5, 15, 10, 0
v_4 = 2, 6, 18, 8, 6
(a) find a basis for the set of vectors and state the dimension of the space spanned by these vectors, what is the rank of this matrix?
(b) construct a matrix whose rows correspond to the vectors v_1 thru v_4 and find the basis for the kernel of that matrix and state the dimension of this null space. what is the rank of this matrix?

Homework Equations



allowed to use calculator

The Attempt at a Solution



According to the book instructions I already solved part A
by taking the RREF of the matrix:
[1, -2, 0, 0, 3]
[2, -5, -3, -2, 6]
[0, 5, 15, 10, 0]
[2, 6, 18, 8, 6]
and noticing that the last row is just zeros, therefore its a rank 3 matrix with dimension n-r which would be 2. (Im not sure if this is 2 or 5. . .since it has 5 variables??)
making a system of eq. with the rref form I get
that Ax = 0 then x=
x_1 = 2t - 3s
x_2 = t
x_3 = -t
x_4 = t
x_5 = s

therefore the basis is:

t[2, 1 ,-1, 1, 0] + s[-3, 0, 0, 0, 1] <-- as column vectors

then for part b. . .when I started to do it I realized it was the exact same process? cause my original matrix was made of the rows and the kernel is the nullspace which is 2 (from what I understood from the dif. chapters in the book its the same as the dimension stated above)
so nullity = n - rank = 2
and then the rank. . .would be 3 again?
what trips me off is that its the exact same process twice? or am I confusing some things here?
 
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  • #2
_Bd_ said:

Homework Statement



for the set of vectors:
v_1 = 1, -2, 0, 0, 3
v_2 = 2, -5, -3, -2, 6
v_3 = 0, 5, 15, 10, 0
v_4 = 2, 6, 18, 8, 6
(a) find a basis for the set of vectors and state the dimension of the space spanned by these vectors, what is the rank of this matrix?
The rank of what matrix? There is no matrix given here. If the problem means the matrix having those vectors as rows or columns, that should have been said.

(b) construct a matrix whose rows correspond to the vectors v_1 thru v_4 and find the basis for the kernel of that matrix and state the dimension of this null space. what is the rank of this matrix?

Homework Equations



allowed to use calculator

The Attempt at a Solution



According to the book instructions I already solved part A
by taking the RREF of the matrix:
[1, -2, 0, 0, 3]
[2, -5, -3, -2, 6]
[0, 5, 15, 10, 0]
[2, 6, 18, 8, 6]
and noticing that the last row is just zeros, therefore its a rank 3 matrix with dimension n-r which would be 2. (Im not sure if this is 2 or 5. . .since it has 5 variables??)
Yes, the rank is three. Now what dimension are you talking about? A matrix does not have a "dimension". Assuming you are talking about the matrix having these vectors as columns it is a linear transformation from [itex]R^4[/itex] to [itex]R^5[/itex]. Since its rank is 3, it nullspace has dimension 5- 3= 2. The "dimension spanned by the vectors", since you have shown that 3 of them are independent, is 3.

making a system of eq. with the rref form I get
that Ax = 0 then x=
x_1 = 2t - 3s
x_2 = t
x_3 = -t
x_4 = t
x_5 = s

therefore the basis is:

t[2, 1 ,-1, 1, 0] + s[-3, 0, 0, 0, 1] <-- as column vectors
Yes, so you have shown that the null space has dimension 2. But there is nothing said about "null space" in part (a).

then for part b. . .when I started to do it I realized it was the exact same process? cause my original matrix was made of the rows and the kernel is the nullspace which is 2 (from what I understood from the dif. chapters in the book its the same as the dimension stated above)
so nullity = n - rank = 2
and then the rank. . .would be 3 again?
what trips me off is that its the exact same process twice? or am I confusing some things here?
If you use those same vectors as rows rather than columns, then you have a linear transformation from \(\displaystyle R^5\) to \(\displaystyle r^4\). You will now find that 3 of the rows are independent so that the rank is 3 and the dimension of the null space is 4- 3= 1.
 
  • #3
HallsofIvy said:
(A) The rank of what matrix? There is no matrix given here. If the problem means the matrix having those vectors as rows or columns, that should have been said.
======

(B) Yes, the rank is three. Now what dimension are you talking about? A matrix does not have a "dimension". Assuming you are talking about the matrix having these vectors as columns it is a linear transformation from [itex]R^4[/itex] to [itex]R^5[/itex]. Since its rank is 3, it nullspace has dimension 5- 3= 2. The "dimension spanned by the vectors", since you have shown that 3 of them are independent, is 3.

=====
(C)
Yes, so you have shown that the null space has dimension 2. But there is nothing said about "null space" in part (a).

(D)
If you use those same vectors as rows rather than columns, then you have a linear transformation from \(\displaystyle R^5\) to \(\displaystyle r^4\). You will now find that 3 of the rows are independent so that the rank is 3 and the dimension of the null space is 4- 3= 1.

(A) I really don't know what matrix If I had to guess it would be the matrix formed by the vectors in space (so that's how I started the problem) and well from what I saw in the book, and as you saw in my example I wrote the vectors in row form not in column form to begin with. . . and went on from there to the rref, dimension of 2 and finding the basis of the matrix formed by those vectors. . .

(B) again I must mention or I don't understand but I wrote it in row form not in column form. . . so I am not too sure as to where(or why) that linear transformation took place

(C)
Ok I found another theorem on the book about the vector space instead of the solution space, got that cleared up now. . .

(D)
again I don't understand the switching of rows to columns for the first part, (part B ask specifically about row vectors which would be the process I already did isn't it?)

= thanks for the help BTW =
 

FAQ: How Do You Determine the Basis, Dimension, and Rank of Vector Sets?

What is nullspace?

Nullspace, also known as the kernel, is the set of all vectors that when multiplied by a given matrix result in the zero vector. In other words, it is the set of all solutions to the homogeneous equation Ax=0.

What is the difference between nullspace and kernel?

Nullspace and kernel are two terms used interchangeably to refer to the same concept. They both represent the set of all vectors that map to the zero vector when multiplied by a given matrix.

How is the nullspace related to the rank of a matrix?

The rank of a matrix is equal to the number of linearly independent columns in the matrix. The dimension of the nullspace is equal to the number of linearly dependent columns in the matrix. Therefore, the rank and nullspace are related in that the sum of the rank and nullspace dimensions is equal to the number of columns in the matrix.

What is the significance of nullspace?

The nullspace is significant because it represents the set of all solutions to the homogeneous equation Ax=0. This means that the nullspace is useful in solving systems of linear equations and in understanding the properties of a given matrix.

How can the nullspace be calculated?

The nullspace can be calculated by finding the reduced row echelon form of a matrix and identifying the free variables. The nullspace is then represented by a linear combination of the free variables in the reduced row echelon form.

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