Proving Rank(A)=2 for a 3x3 Matrix with Non-Zero Elements

In summary: yup, i think that bottom c should be -c :wink:in fact, if you multiply the matrix by the vector (c,b,-a), you can see that that's an eigenvector with eigenvalue 0
  • #1
dietcookie
15
0

Homework Statement


Assume that a, b, and c do not equal zero.

Let matrix A=

0 a b
-a 0 c
-b c 0

Prove that Rank(A)=2

Homework Equations


Definition: The rank of a matrix A is the number of linearly independent rows or columns of A

The Attempt at a Solution



I've attempted to get it into reduced row echelon form with the following row ops (as row ops preserve the rank)

r2<-->r1
(-b)r1-->r1
(c)r3+r1-->r3
(1/2)r3-r2-->r3
(1/c)r3-r2-->r3
(1/2)r3-->r3

I end up with this matrix

ab 0 -bc
0 a b
0 0 b

If I could get r3 all zeros, then I would have two non-zero rows in rref form, which would mean the rank(a)=2, but I'm stuck here. Am I approaching this right? Thanks.

I keep staring at this and now I'm thinking perhaps I wrote down the problem wrong. As we know there are the equivalent conditions for square matrices, one of them being, det(a) does not equal zero and Rank(A)=n. Well the determinant of the example does not equal zero, det(A)=-2abc so doesn't that mean that the rank of this matrix should equal three?? (as it is 3x3?)
 
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  • #2
hi dietcookie! :smile:
dietcookie said:
I keep staring at this and now I'm thinking perhaps I wrote down the problem wrong. … Well the determinant of the example does not equal zero, det(A)=-2abc so doesn't that mean that the rank of this matrix should equal three?? (as it is 3x3?)

yup, i think that bottom c should be -c :wink:
 
  • #3
in fact, if you multiply the matrix by the vector (c,b,-a), you can see that that's an eigenvector with eigenvalue 0 …

this matrix is the standard matrix for eg a magnetic field … it simply gives the cross-product of any vector with (c,b,-a) :wink:
 

1. What is the definition of the rank of a matrix?

The rank of a matrix is the maximum number of linearly independent rows or columns in the matrix. In other words, it is the number of rows or columns that contain non-zero values and cannot be expressed as a linear combination of other rows or columns.

2. How is the rank of a matrix related to its dimensions?

The rank of a matrix can never exceed the smaller of its number of rows or columns. For example, a 3x5 matrix can have a maximum rank of 3, while a 5x3 matrix can have a maximum rank of 3 as well.

3. Can a matrix have a negative rank?

No, the rank of a matrix is always a non-negative integer. If a matrix has a rank of 0, it means that all of its rows and columns are linearly dependent, and if it has a rank of n (where n is the number of rows or columns), it means that all of its rows and columns are linearly independent.

4. How can I prove the rank of a matrix?

There are several methods for proving the rank of a matrix, including row reduction, Gaussian elimination, and using the determinant. The most common method is to use row reduction to transform the matrix into reduced row-echelon form, and the number of non-zero rows in the resulting matrix will be equal to the rank of the original matrix.

5. What is the significance of the rank of a matrix?

The rank of a matrix is an important property that can provide information about the linear dependence or independence of its rows and columns. It is also used to determine the dimensionality of the solution space for a system of linear equations represented by the matrix. Additionally, the rank of a matrix is used in many applications, such as data compression and image processing, to identify and eliminate redundant information.

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