Compute rank(T) by reducing matrix to echelon form

What you did is just fine. The rank of T is 4.In summary, the rank of the transformation T from R4 to R4, defined by T(a,b,c,d) = (4a-b+c, b+3d, 3a+c+d, c-d, b+c+2d), is 4.
  • #1
p3forlife
20
0

Homework Statement


Let T: R4 --> R4 be defined by T(a,b,c,d) = (4a-b+c, b+3d, 3a+c+d, c-d, b+c+2d)
where a,b,c,d are in R. Compute rank(T).


Homework Equations


The Attempt at a Solution


Writing the transformation as a matrix,

T[a] = [4 -1 1 0][a]
[0 1 0 3]
[c] [3 0 1 1][c]
[d] [0 0 1 -1][d]
[0 1 1 2]

So that big matrix on top, call it A, is the standard matrix. We'll need to reduce A to echelon form in order to find the rank(T).
So this is how far I got:
A = [4 -1 1 0]
[0 1 3 0]
[3 0 2 0]
[0 0 -1 1]
[0 1 3 0]
I can't seem to reduce this any further without replacing the zero elements with nonzero elements, which is not the point. Rank(T) should be the number of nonzero rows in the matrix reduced to echelon form, right? So does that mean that rank(T) = 5? Although, I don't think A is completely reduced. Maybe my arithmetic does not work.
 
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  • #2
Perhaps you can try computing the dimension of the null space of T and then using the dimension theorem to find the rank of T.
 
  • #3
p3forlife said:

Homework Statement


Let T: R4 --> R4 be defined by T(a,b,c,d) = (4a-b+c, b+3d, 3a+c+d, c-d, b+c+2d)
where a,b,c,d are in R. Compute rank(T).


Homework Equations


The Attempt at a Solution


Writing the transformation as a matrix,

T[a] = [4 -1 1 0][a]
[0 1 0 3]
[c] [3 0 1 1][c]
[d] [0 0 1 -1][d]
[0 1 1 2]

So that big matrix on top, call it A, is the standard matrix. We'll need to reduce A to echelon form in order to find the rank(T).
So this is how far I got:
A = [4 -1 1 0]
[0 1 3 0]
[3 0 2 0]
[0 0 -1 1]
[0 1 3 0]
I can't seem to reduce this any further without replacing the zero elements with nonzero elements, which is not the point. Rank(T) should be the number of nonzero rows in the matrix reduced to echelon form, right? So does that mean that rank(T) = 5? Although, I don't think A is completely reduced. Maybe my arithmetic does not work.

You need more practice on row reducing! Always work on one column at a time from left to right and top to bottom. Starting with
[tex]\left[\begin{array}{cccc}4 & -1 & 1 & 0 \\ 0 & 1 & 0 & 3 \\ 3 & 0 & 1 & 1 \\ 0 & 0 & 1 & -1 \\ 0 & 1 & 1 & 2\end{array}\right][/tex]
The left column has 4 at the top and 3 in the third row- 0s else where. Subract 3/4 the first row from the third: it becomes 3-3= 0, 0- (-3/4)= 3/4, 1- 3/4= 1/4, 1- 0= 0. The matrix is now
[tex]\left[\begin{array}{cccc}4 & -1 & 1 & 0 \\ 0 & 1 & 0 & 3 \\ 0 & 3/4 & 1/4 & 1 \\ 0 & 0 & 1 & -1 \\ 0 & 1 & 1 & 2\end{array}\right][/tex]
Yes, some 0s were filled but the first column is "1 0 0 0" and THAT is the point.
Now look at the second column. There is a 1 in the second row and by subtracting 3/4 of the second row from the third, I can't get a 0 in the third row, second column while subracting the second row from the fourth gives me a 0 there:
[tex]\left[\begin{array}{cccc}4 & -1 & 1 & 0 \\ 0 & 1 & 0 & 3 \\ 0 & 0 & 1/4 & -5/4 \\ 0 & 0 & 1 & -1 \\ 0 & 0 & 1 & -1\end{array}\right][/tex]
Now do the third column. There is a 1/4 in "third row third column. I can get a 0 in the fourth row by subtracting 4 times the third row and a 0 in the fifth row by doing the same:
[tex]\left[\begin{array}{cccc}4 & -1 & 1 & 0 \\ 0 & 1 & 0 & 3 \\ 0 & 0 & 1/4 & -5/4 \\ 0 & 0 & 0 & 4 \\ 0 & 0 & 0 & 4\end{array}\right][/tex]
The last step should be obvious: subtract the fourth row from the fifth:
[tex]\left[\begin{array}{cccc}4 & -1 & 1 & 0 \\ 0 & 1 & 0 & 3 \\ 0 & 0 & 1/4 & -5/4 \\ 0 & 0 & 0 & 4 \\ 0 & 0 & 0 & 0\end{array}\right][/tex]k

By the way- it should have been obvious that the rank could NOT be 5. This transformation maps R4 to a subspace of R5. The dimension of that subspace is the "rank" and it cannot be higher than 4.
 
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FAQ: Compute rank(T) by reducing matrix to echelon form

1. What is the purpose of computing the rank of a matrix?

The rank of a matrix is the number of linearly independent rows or columns in the matrix. It is used to determine the dimension of the vector space spanned by the matrix's rows or columns. In other words, it tells us how many unique directions or dimensions are present in the matrix.

2. How is the rank of a matrix calculated?

The rank of a matrix can be calculated by reducing the matrix to echelon form and counting the number of non-zero rows. Alternatively, it can also be calculated by finding the maximum number of linearly independent columns or rows in the matrix.

3. What is the significance of reducing a matrix to echelon form?

Reducing a matrix to echelon form is a way to simplify and organize the information in the matrix. It allows for easier calculation of the rank, as well as other important properties such as the determinant and inverse of the matrix.

4. Can the rank of a matrix be greater than the number of rows or columns?

No, the rank of a matrix cannot be greater than the number of rows or columns. The rank is always a value between 0 and the minimum of the number of rows or columns. This is because the rank is a measure of the dimension of the vector space spanned by the matrix's rows or columns, and it cannot exceed the maximum dimension of the matrix itself.

5. How does the rank of a matrix relate to its invertibility?

A matrix is invertible if and only if its rank is equal to the number of rows/columns, which is also known as the matrix's order. In other words, a square matrix with full rank is always invertible. For non-square matrices, the rank and invertibility are related in a more complex way, but the rank still provides important information about the matrix's properties.

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