Linear algebra rank and dimensions

In summary, the conversation discusses the relationship between rank and nullity in a matrix A in R^(mxn). It is proven that Rank A + dim Nul A^T = m, using the rank-nullity theorem and the concept of row and column rank. The conversation also mentions the use of transpose and the relationship between the row space and column space of a matrix. The concept of row space is ignored in this discussion.
  • #1
SpiffyEh
194
0

Homework Statement


Prove Rank A + dim Nul A^T = m where A is in R^(mxn)


Homework Equations





The Attempt at a Solution



I honestly can't figure out where to go with this. I know that Rank A + dim Nul A = n, but I don't know if there is a relationship between the two.
 
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  • #2
Hint: If rank A = x, what does rank AT equal?
 
  • #3
Does rank A^T = x as well?
 
  • #4
Yes.
 
  • #5
I don't see how that relationship works out, i saw it in a theorem online but i wasn't able to find it in my book. Also I don't see where to go from there
 
  • #6
Does your book talk about "row rank" and "column rank" and then say we just call it "rank" because they are always equal? Anyway, now just use your rank-nullity theorem on AT. (ie, if A is mxn, then AT is nxm, etc)
 
  • #7
Yeah it has a section about rank but it never mentions the transpose. So, If i use the rank-nullity theorm on A^T could I just transpose the rank and the nullity and make it equal to m instead of n? Then since the transpose of the rank is equal to it not being transposed just switch it to that? Would that be enough to prove the concept?
 
  • #8
Yes. Recall that the column rank of A is the dimension of the column space of A, and the row rank is the dimension of the row space of A. These are equal. The proof of this is longer than this sentence, so I refer you to other references for that.

Now the row space of AT is the column space of A, right? Because the rows of AT are the columns of A. So the rank of A is the rank of AT.
 
  • #9
ok ok thank you that makes sense. We were told to ignore the whole concept of row space so I didn't even think of using that
 

1. What is the rank of a matrix in linear algebra?

The rank of a matrix in linear algebra refers to the maximum number of linearly independent rows or columns in the matrix. In other words, it is the dimension of the vector space spanned by the rows or columns of the matrix.

2. How is the rank of a matrix determined in linear algebra?

The rank of a matrix can be determined by performing row operations on the matrix until it is in reduced row echelon form. The number of non-zero rows in the reduced matrix will be equal to the rank of the original matrix.

3. What is the relationship between the rank and the dimensions of a matrix?

The rank of a matrix is equal to the minimum of its row and column dimensions. In other words, the rank cannot be greater than the number of rows or columns in the matrix.

4. Can a matrix have a rank greater than its dimensions?

No, a matrix cannot have a rank greater than its dimensions. This is because the rank is determined by the maximum number of linearly independent rows or columns, which cannot exceed the number of rows or columns in the matrix.

5. How is the concept of rank used in linear algebra?

The concept of rank is used in linear algebra to determine important properties of a matrix, such as its invertibility and solutions to systems of linear equations. It is also used in various applications, such as data analysis and machine learning, to understand the relationships between variables in a dataset.

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