Rank of the product of two matrices

In summary, the conversation discusses the proof of a corollary stating that if A is an M by n matrix and B is a square matrix of rank n, then rank(AB) = rank(A). One person is seeking ideas on how to prove this theorem, which is a corollary to another theorem about multiplying matrices. The second person suggests using the fact that an n by n matrix of rank n is invertible to prove the corollary.
  • #1
aukie
1
0
Hello

Both of the below theorems are listed as properties 6 and 7 on the wikipedia page for the rank of a matrix.

I want to prove the following,

If A is an M by n matrix and B is a square matrix of rank n, then rank(AB) = rank(A).

Apparently this is a corollary to the theorem
If A and B are two matrices which can be multiplied, then rank(AB) <= min( rank(A), rank(B) ).

which I know how to prove. But I can't prove the first theorem. Any ideas? I would especially like to see how it is a corollary to the second theorem which the author in the book I am reading claims. Thanks for reading
 
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  • #2
aukie said:
I want to prove the following,

If A is an M by n matrix and B is a square matrix of rank n, then rank(AB) = rank(A).

Apparently this is a corollary to the theorem
If A and B are two matrices which can be multiplied, then rank(AB) <= min( rank(A), rank(B) ).
You want to prove that if A is an M by n matrix and B is an n by n matrix of rank n, then rank(AB) = rank(A). But an n by n matrix of rank n is necessarily invertible. So $B$ has an inverse $B^{-1}$. It follows from the theorem that $\text{rank}(A) = \text{rank}((AB)B^{-1}) \leqslant \text{rank}(AB).$ The reverse inequality $\text{rank}(AB)\leqslant \text{rank}(A)$ follows directly from the theorem. Hence $\text{rank}(AB)= \text{rank}(A).$
 

FAQ: Rank of the product of two matrices

What is the rank of the product of two matrices?

The rank of the product of two matrices is the highest number of linearly independent rows or columns in the resulting matrix. It is a measure of the dimensionality of the space spanned by the rows or columns of the product matrix.

How is the rank of the product of two matrices calculated?

The rank of the product of two matrices can be calculated by multiplying the ranks of the individual matrices. This means that if the first matrix has a rank of m and the second matrix has a rank of n, then the rank of their product will be min(m, n).

Is the rank of the product of two matrices always equal to the product of their individual ranks?

No, the rank of the product of two matrices can be less than the product of their individual ranks. In some cases, it can even be zero.

What does it mean if the rank of the product of two matrices is zero?

If the rank of the product of two matrices is zero, it means that the two matrices are singular and their product will result in a matrix with all zero values. This indicates that the two matrices do not have enough linearly independent rows or columns to produce a non-zero product.

Can the rank of the product of two matrices be greater than the rank of either individual matrix?

Yes, it is possible for the rank of the product of two matrices to be greater than the rank of either individual matrix. This can happen if the two matrices have linearly independent rows or columns that, when multiplied, create additional linearly independent rows or columns in the product matrix.

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