A short one on matrix multiplication

In summary, the conversation discusses the proof that Bm = (I - B1A1T)(AmT)+, where B = (AT)-1 and B1, Bm, A1, and Am are block matrices. The discussion involves strategies for proving this statement and understanding the meaning of the '+' symbol exponent on a column vector. It is ultimately determined that using partitioning and the fact that (AmT)+ is a good enough inverse for AmT, the proof can be successfully carried out.
  • #1
Päällikkö
Homework Helper
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Let A and B be real invertible n x n matrices so that B = (AT)-1.
Show that Bm = (I - B1A1T)(AmT)+, where
B1 = [b1, ..., bm],
Bm = [bm+1, ..., bn],
A1 = [a1, ..., am],
Am = [am+1, ..., an].


Any pointers on how one would go about proving the above? I'm fresh out of ideas.

I'm not sure if the above statement is even actually true. Using random matrices in matlab, I have not found any counterexamples, though.
 
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  • #2
What does the '+' symbol exponent on a column vector mean?
 
  • #3
I'm not sure if that's a hint or a question: the pseudoinverse.
 
  • #4
It's a question - just trying to understand the problem.
 
  • #5
So, as block matrices,

A = [ A1 | Am ]
B = [ B1 | Bm ]

Have you tried just multiplying things out in block form?
 
  • #6
How do you get to a situation where you get to use the matrices A and B?

Do you mean something like
(I - B1A1T)(AmT)+ = (I - BF(AF)T)((AG)T)+,
where F = [I O]T (where I is m x m and F is n x m) and G = [O I]T (where I is (n-m) x (n-m) and G is n x (n-m)).

I doubt you meant that, could you elaborate? Anyways, the above I've tried to no avail.

EDIT: or do you mean starting from the equation B = [B1 Bm] = (AT)-1 = ([A1 Am]T)-1 and trying to solve for Bm? That I haven't actually tried but it seems a bit like a long shot. I'll see if I can get it going.

EDIT: No, can't get it going.
 
Last edited:
  • #7
Ok, try getting it going this way. Look at matrices as blocks, as Hurkyl suggests. Your premise shows B.A^T=1. The partitioning splits this into B1.A1^T+Bm.Am^T=1. So Bm.Am^T=1-B1.A1^T. Now you just have to show that (Am^T)^+ is a good enough inverse to Am^T that you can multiply both sides by it and get what you want. I'm still scratching my head over how to write this down formally, but maybe you can figure it out.
 
  • #8
Yeah, and the last step is easy as well. Am^T has linearly independent rows. Writing out (Am^T)^+ explicitly (as in the wikipedia entry on pseudoinverses) and then multiplying Am^T by it gives an n-mxn-m unit matrix.
 
  • #9
Ah indeed, partitioning it in that manner makes it all clear. Thank you very much.
 

1. What is matrix multiplication?

Matrix multiplication is a mathematical operation where two matrices are multiplied to produce a new matrix. It is used to combine two sets of data and is an important tool in linear algebra and other areas of mathematics.

2. How is matrix multiplication different from regular multiplication?

Matrix multiplication is different from regular multiplication in that it is not commutative. This means that the order in which the matrices are multiplied matters. Also, the size of the matrices must match in order for the operation to be possible.

3. What are some applications of matrix multiplication?

Matrix multiplication has many practical applications, such as in computer graphics, data analysis, and physics. It is also used in various fields of engineering, including electrical and mechanical engineering.

4. How do you perform matrix multiplication?

To multiply two matrices, you need to first ensure that the number of columns in the first matrix matches the number of rows in the second matrix. Then, multiply corresponding elements in each row of the first matrix with corresponding elements in each column of the second matrix, and add the products together to get the corresponding element in the resulting matrix.

5. Can any two matrices be multiplied together?

No, not all matrices can be multiplied together. For matrix multiplication to be possible, the number of columns in the first matrix must match the number of rows in the second matrix. This is known as the "inner dimensions" rule.

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