Dimension of the null space of A transpose

In summary, to find the null space of the transpose of a matrix A that is already in RREF, you do not need to RREF the transpose. The dimension of the null space can be determined by understanding the rank of the original matrix, as the null space of the transpose is the orthogonal complement of the range of the matrix. This can also be seen through the fact that the rank of the transpose is equal to the rank of the original matrix.
  • #1
leroyjenkens
616
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So I'm given a matrix A that is already in RREF and I'm supposed to find the null space of its transpose.
So I transpose it. Do I RREF the transpose of it? Because if I transpose a matrix that's already in RREF, it's no longer in RREF. But if I RREF the transpose, it gives me a matrix with 2 leading entries that are both equal to zero. I don't think I'm doing it right.

Thanks.
 
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  • #2
you don't need to do anything to find the dimension of the nullspace of the transpose if you already understand the rank of the matrix, since the nullspace of the transpose is the orthogonal complement of the range of the matrix. so if an nbym matrix represents a map R^m-->R^n of rank r, then the range has dimension r, so its orthocomplement has dimension n-r.

Equivalently, the rank of the transpose is also r. I.e. RREF shows you that both the row rank and the column rank are equal. The rank of a matrix is by definition the column rank, but the columns of the transpose equal the rows of the original matrix.

I.e. just knowing the rank of the original matrix, gives you also the rank of the transpose and hence also its nullity.
 

1. What is the dimension of the null space of A transpose?

The dimension of the null space of A transpose is also known as the nullity of A transpose, and it represents the number of linearly independent columns in the original matrix A.

2. How is the dimension of the null space of A transpose related to the rank of A?

The dimension of the null space of A transpose and the rank of A are related through the rank-nullity theorem, which states that the sum of the rank and nullity of a matrix is equal to the number of columns in the matrix.

3. What does a larger dimension of the null space of A transpose indicate?

A larger dimension of the null space of A transpose indicates that there are more linearly independent columns in the original matrix A, and therefore, the matrix has more freedom in its column space.

4. How can the dimension of the null space of A transpose be calculated?

The dimension of the null space of A transpose can be calculated by finding the null space of the original matrix A, and then counting the number of linearly independent vectors in the resulting basis.

5. Can the dimension of the null space of A transpose be zero?

Yes, the dimension of the null space of A transpose can be zero if the original matrix A has full rank, meaning that all of its columns are linearly independent. In this case, the null space of A is empty, and therefore, the dimension of the null space of A transpose is also zero.

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