Prove A Orthogonal $\Rightarrow$ |A|=+-1

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I am not able to help you with that.In summary, if A is an orthogonal matrix, then its determinant is either +1 or -1. Additionally, the inverse of A is equal to its transpose, and the product of A and its inverse is equal to the identity matrix. This can be shown by using the fact that |A| = |A^T| and simplifying the expression |I| = |A|*|A^T| to reach the conclusion |A|^2 = +-1.
  • #1
eyehategod
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Prove if A is orthogonal matrix, then |A|=+-1
A[tex]^{-1}[/tex]=A[tex]^{T}[/tex]
AA[tex]^{-1}[/tex]=AA[tex]^{T}[/tex]
I=AA[tex]^{T}[/tex]
|I|=|AA[tex]^{T}[/tex]|
1=|A|*|A[tex]^{T}[/tex]|//getting to the next step is where i get confused. Why is |A|=|A[tex]^{T}[/tex]|
1=|A|*|A|
1=|A|[tex]^{2}[/tex]
+-1=|A|
 
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  • #2
I think there is an error in your question. For matrices to be orthogonal A_inverse= A_transpose.

Sorry I am not yet familiar with LATEX.
 
  • #3
eyehategod said:
getting to the next step is where i get confused. Why is |A|=|A[tex]^{T}[/tex]|
This is a standard result. You should probably find it in your textbook, or try to prove it yourself.
 

1. What does "orthogonal" mean in this context?

In mathematics, orthogonal refers to two objects being at right angles to each other. In linear algebra, it specifically refers to two vectors being perpendicular to each other.

2. How do you prove that A being orthogonal implies that its determinant is equal to +/- 1?

There are a few different methods for proving this statement. One approach is to use the dot product of the two vectors in A and show that it is equal to 0 if they are orthogonal. From there, you can use the properties of determinants to show that |A| = +/- 1.

3. Can you provide an example of a matrix A that is orthogonal and has a determinant of 1?

One example of such a matrix is the identity matrix, which has a determinant of 1 and is orthogonal because its columns are all orthogonal to each other.

4. Why is it important to prove this statement?

This statement is important because it is a fundamental property of orthogonal matrices, which have many applications in mathematics, physics, and engineering. It also helps us understand more about the properties of determinants and how they relate to other mathematical concepts.

5. Are there any exceptions to this statement?

Yes, there are exceptions to this statement. For example, the zero matrix is orthogonal but its determinant is 0, not +/- 1. Additionally, in dimensions higher than 3, there can be orthogonal matrices with determinants other than +/- 1. However, the statement holds true in most cases and is an important property to understand.

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