Matrix multiplication: Communicative property.

Click For Summary

Discussion Overview

The discussion revolves around the commutative property of matrix multiplication, particularly in the context of unitary matrices and their multiplication with vectors. Participants explore whether commutativity holds in specific cases, such as when using the Discrete Fourier Transform (DFT) matrix and other forms of matrices.

Discussion Character

  • Debate/contested
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant states that literature claims matrix multiplication is commutative only for diagonal matrices, but they question this in the context of unitary matrices.
  • Another participant argues that many matrices are not commutative, providing examples of specific 2x2 matrices that can commute.
  • A counter-example is presented involving a diagonal matrix with a zero entry, which leads to a clarification about the nature of diagonal matrices.
  • Participants discuss the dimensions of matrices involved in multiplication, noting that a (1xM) vector cannot commute with an (MxM) matrix unless M=1.
  • One participant reflects on their earlier misunderstanding of the term "commutative" in relation to the dimensions of the matrices being multiplied.
  • Another participant mentions that symmetric matrices allow for commutative operations under certain conditions.
  • There is a discussion about the implications of using vectors as matrices and how this affects the application of matrix multiplication properties.

Areas of Agreement / Disagreement

Participants express differing views on the conditions under which matrix multiplication is commutative, with no consensus reached on the general applicability of the commutative property across different types of matrices.

Contextual Notes

Some participants note limitations regarding the definitions and dimensions of matrices involved in the discussion, which may affect the validity of claims about commutativity.

m26k9
Messages
9
Reaction score
0
Matrix multiplication: Commutative property.

Hello,

First time poster.
I have got a question about commutative property of matrix multiplication.
Literature says that matrix multiplication is communicative only when the two matrices are diagonal.

But, I have a situation with an 'Unitary' matrix. Actually it is the DFT matrix http://en.wikipedia.org/wiki/Discrete_Fourier_transform#The_unitary_DFT". And I multiply with a 'vector'.

It seems that communicative property holds in this case. But I want to know what is the theoretical explanation, or the property as to why communicative property holds in this case.

Thank you very much.
 
Last edited by a moderator:
Physics news on Phys.org
Literature says that matrix multiplication is communicative only when the two matrices are diagonal.
You certainly misread that, because it is not true that the diagonal matrix is the only[/tex] type of matrices that are commutative, surely many matrices are not commutative, but some are.
Take for example the set of 2x2 matrices of the form
[tex]\begin{array}{cc}<br /> a & b \\<br /> -b & a<br /> \end{array}[/tex]
 
Last edited:
Another silly counter-example:

Matrices of the form:


[tex]\begin{array}{cc}<br /> a & 0 \\<br /> 0 & 0<br /> \end{array}[/tex]
 
The silly counter-example is a diagonal matrix in which one of the entries on the diagonal happens to be 0.

A clarification might be useful here. You have a matrix that is multiplied by a vector? That might be an (m x m) matrix multiplied by an (m x 1) vector. You can't multiply an (m x 1) with an (m x m).
 
hokie1 said:
The silly counter-example is a diagonal matrix in which one of the entries on the diagonal happens to be 0.

A clarification might be useful here. You have a matrix that is multiplied by a vector? That might be an (m x m) matrix multiplied by an (m x 1) vector. You can't multiply an (m x 1) with an (m x m).

Heh, oops. The silly example is for some reason I was using an alternative definition for "diagonal" X-D
 
Thats OK. Usually I'm the one saying oops.
 
Thanks a lot for the replies guys.

I am multiplying a (1xM) vector with the (MxM) unitary matrix.
For the few random matrices I tried, it seems to be commutative. Atleast for the DFT matrix (Vandermond) I tried.
I want to know if there is any property talking about this scenario.

Thank you very much.
 
m26k9 said:
For the few random matrices I tried, it seems to be commutative.

Can your matrixes be diagonalized with the same similarity transformation?

[tex] \begin{align*}<br /> U_1 &= V D_1 V^* \\<br /> U_2 &= V D_2 V^*<br /> \end{align*}[/tex]

EDIT: fixed wrong lingo in question.
 
Last edited:
m26k9 said:
Thanks a lot for the replies guys.

I am multiplying a (1xM) vector with the (MxM) unitary matrix.
For the few random matrices I tried, it seems to be commutative.
You are using the term "commutative" incorrectly here. Given an operator * and operands a and b, a and b commute if a*b=b*a. If a is 1xM and b is MxM, the product a*b exists (and is 1xM) but b*a exists only if M=1. In other words, there is no way a 1xM and a MxM matrix can commute unless M=1 (i.e., if a and b are scalars).
 
  • #10
That's what I was getting at. That's why I was mentioning the dimensions of the matrices. Too often software packages bend the rules and treat vectors as both (1 x m) and (m x 1) matrices to fit the math. In that case a symmetric (m x m) matrix allows the operation to be commutative, i.e. A[i,j] = A[j,i].
 
  • #11
Sorry guys.

Yes, if I am using say A is a (1xM) vector, it cannot be commutative.
My mistake. Actually I am AxB and BxA', conjugate of A.
So this is not commutation anymore?
If this is the case, that means matrix multiplication properties cannot be applied, unless I put my vector entries inside a diagonal of a matrix?

Thanks lot guys.
 

Similar threads

  • · Replies 14 ·
Replies
14
Views
4K
  • · Replies 7 ·
Replies
7
Views
3K
  • · Replies 15 ·
Replies
15
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 7 ·
Replies
7
Views
4K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 8 ·
Replies
8
Views
3K