Prove that rank(A) = rank(A^T)

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In summary, the conversation discusses the use of a given relation to prove that the rank of a matrix A is equal to the rank of its transpose, A^T. The conversation includes confusion about the use of operators and terminology, but ultimately concludes that the argument is valid and provides a clearer explanation of the solution.
  • #1
hotvette
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Homework Statement


Use the below relation to prove that [itex]rank(A) = rank(A^T)[/itex]

Homework Equations


[tex](Im A)^c = ker(A^T)[/tex] where c is the orthogonal compliment.

The Attempt at a Solution


Let A be an n x m matrix with [itex](n \ge m)[/itex] and [itex]rank(A) = k[/itex]. Then, [itex]rank(A^c) = n-k[/itex]. From the given relation, [itex]rank(ker A^T)[/itex] must also equal [itex]n-k[/itex]. Thus, [itex]rank(A^T) = n - (n-k) = k = rank(A)[/itex].

Is this a valid argument?
 
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  • #2
It looks like it's probably correct, but the explanation is a bit confusing because it uses operators in what appear to be non-standard ways. For instance if c denotes orthogonal complement, that is a function whose domain is a vector space, so it cannot be applied to a linear operator, which is what the symbol ##A^c## suggests. Also, I have not seen the rank operator applied to a space rather than to a linear operator, which is what ##rank(ker\ A^T)## suggests. Perhaps it means 'dim'(ension)?
 
  • #3
Thanks, I agree that my explanation used sloppy terminology. Maybe this is better: Let [itex]A[/itex] be an n x m matrix with [itex]n \ge m[/itex] and [itex]rank=k[/itex]. Then [itex]dim((Im A)^c) = m-k = dim(ker(A^T))[/itex]. Also, [itex]dim(ker(A^T)^c)=m-(m-k)=k[/itex]. But, [itex](ker(A^T))^c = Im(A^T)[/itex]. Therefore, [itex]dim(Im(A^T))=k=dim(Im A)[/itex], and thus [itex]rank(A^T)=rank(A)[/itex]
 

FAQ: Prove that rank(A) = rank(A^T)

What does it mean for the rank of a matrix to be equal to the rank of its transpose?

When the rank of a matrix A is equal to the rank of its transpose A^T, it means that the number of linearly independent rows and columns in both matrices are the same. This also implies that the dimension of the column space and the row space are equal.

How can you prove that rank(A) = rank(A^T)?

To prove that the rank of a matrix is equal to the rank of its transpose, we can use the fact that the row rank and column rank of a matrix are always equal. This can be shown by using elementary row and column operations to reduce the matrix to its row-echelon form. The number of non-zero rows or columns in the reduced matrix will be the rank, and this will be the same for both A and A^T.

Can the equality of rank(A) and rank(A^T) be extended to any matrix A, or are there specific conditions?

This equality holds for any matrix A, regardless of its size or the values of its entries. It is a fundamental property of matrices that can be proven using basic linear algebra concepts.

What is the significance of rank(A) = rank(A^T) in terms of the properties of A?

The fact that the rank of a matrix is equal to the rank of its transpose has several implications. It means that the null space and the left null space of A are also equal. It also implies that the determinant of A is equal to the determinant of A^T, and the eigenvalues of A and A^T are the same. Additionally, this equality is useful in solving systems of linear equations and finding the inverse of a matrix.

Are there any other properties or relationships between A and A^T that can be derived from rank(A) = rank(A^T)?

Yes, there are several other properties and relationships that can be derived from this equality. For example, it can be shown that the column space of A is orthogonal to the null space of A^T, and the row space of A is orthogonal to the null space of A. This can also be extended to the orthogonal complements of these spaces. Additionally, this equality can be used to prove that the range and null space of a matrix are complementary subspaces.

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