Is this a complete test to show that a matrix is orthogonal?

In summary: That is actually a very good question. There is no reason why MM^T=I should imply M^TM=I. But this is actually quite a deep result in linear algebra. The result is that every left-invertible matrix is invertible. And likewise with right-invertible.So if AB=I, then it holds that BA=I. This is a special result that only holds for matrices.
  • #1
tamtam402
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I used to test orthogonality by using the definition MT = M-1, which means I always calculated the inverse of the matrices. However, isn't it true that if M is orthogonal, then MMT = I?

If we multiply both side by M-1, we get MT = M-1.

Can I use this to proof the orthogonality of a matrix M, instead of calculating it's (often tedious) inverse?
 
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  • #2
tamtam402 said:
I used to test orthogonality by using the definition MT = M-1, which means I always calculated the inverse of the matrices. However, isn't it true that if M is orthogonal, then MMT = I?

If we multiply both side by M-1, we get MT = M-1.

Can I use this to proof the orthogonality of a matrix M, instead of calculating it's (often tedious) inverse?



Of course...it's exactly the same, right?!

DonAntonio
 
  • #3
DonAntonio said:
Of course...it's exactly the same, right?!

DonAntonio

This might be a dumb question, but would it be possible to have a matrix M where MMT = I, yet MTM ≠ I? I'm pretty sure that by definition, proving either of these 2 equalities is enough to know that M-1 = MT, but I wanted to make sure.
 
  • #4
tamtam402 said:
This might be a dumb question, but would it be possible to have a matrix M where MMT = I, yet MTM ≠ I? I'm pretty sure that by definition, proving either of these 2 equalities is enough to know that M-1 = MT, but I wanted to make sure.

That is actually a very good question. There is no reason why [itex]MM^T=I[/itex] should imply [itex]M^TM=I[/itex].
But this is actually quite a deep result in linear algebra. The result is that every left-invertible matrix is invertible. And likewise with right-invertible.
So if AB=I, then it holds that BA=I. This is a special result that only holds for matrices.
 
  • #5
micromass said:
That is actually a very good question. There is no reason why [itex]MM^T=I[/itex] should imply [itex]M^TM=I[/itex].
But this is actually quite a deep result in linear algebra. The result is that every left-invertible matrix is invertible. And likewise with right-invertible.
So if AB=I, then it holds that BA=I. This is a special result that only holds for matrices.


Well, that holds in any group and in any monoid where every element has a right inverse, since then ( with [itex]\,a'=\,[/itex] right inverse of [itex]\,a[/itex] ):
[tex]a'a=a'a(a'a'')=a'(aa')a''=a'ea''=a'a''=e[/tex]
so the right inverse is also the left one.

DonAntonio
 

FAQ: Is this a complete test to show that a matrix is orthogonal?

What is an orthogonal matrix?

An orthogonal matrix is a square matrix whose columns and rows are orthogonal unit vectors. This means that the dot product of any two columns or rows will result in 0, and the length of each column or row is equal to 1.

2. How can I determine if a matrix is orthogonal?

To determine if a matrix is orthogonal, you can use the following criteria:

  • The transpose of the matrix is equal to its inverse (AT = A-1)
  • The columns and rows of the matrix are orthogonal unit vectors
If both of these criteria are met, then the matrix is orthogonal.

3. What is the significance of an orthogonal matrix?

Orthogonal matrices have several important properties, such as preserving the length and angle of vectors when multiplied by the matrix. This makes them useful in various applications, including computer graphics, signal processing, and linear algebra.

4. Can a non-square matrix be orthogonal?

No, a non-square matrix cannot be orthogonal. Orthogonality is a property that applies only to square matrices, where the number of rows is equal to the number of columns.

5. How can I use this test to prove that a matrix is orthogonal?

To show that a matrix is orthogonal using this test, you would need to check if the transpose of the matrix is equal to its inverse and if the columns and rows are orthogonal unit vectors. If both of these criteria are met, then the matrix is orthogonal.

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